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RobusterNet: Improving Copy-Move Forgery Detection with Volterra-Based Convolutions

Efthimia Kafali, Nicholas Vretos, Theodoros Semertzidis, Petros Daras

Auto-TLDR; Convolutional Neural Networks with Nonlinear Inception for Copy-Move Forgery Detection

Abstract Slides

Convolutional Neural Networks (CNNs) have recently been introduced for addressing copy-move forgery detection (CMFD). However, current CMFD CNN-based approaches have insufficient performance commitment regarding the localization of the positive class. In this paper, this issue is explored by considering both linear and nonlinear interactions between pixels. A nonlinear Inception module based on second-order Volterra kernels is proposed, in order to ameliorate the results of a state-of-the-art CMFD architecture. The outcome of this work shows that a combination of linear and nonlinear convolution kernels can make the input foreground and background pixels more separable. The proposed approach is evaluated on CASIA and CoMoFoD, two publicly available CMFD datasets, and results to an improved positive class localization performance. Moreover, the findings of the proposed method imply that the nonlinear Inception module stimulates immense robustness against miscellaneous post processing attacks.

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Countering Anti-Forensics of SIFT-Based Copy-Move Detection

Muhammad Salman, Andreas Uhl

Auto-TLDR; Countering SIFT Keypoint Removal for Image Forgery Detection by Changing to a Different Type of Keypoints

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Forensic analysis is used to detect image forgeries e.g. the copy move forgery and the object removal forgery, respectively. Counter forensic techniques (aka anti-forensic methods to fool the forensic analyst by concealing traces of manipulation) have become popular in the game of cat and mouse between the analyst and the attacker. Classical anti-forensic techniques targeting on SIFT keypoints have been established with particular emphasis on keypoint removal in the context of copy move forgery detection. In this paper we propose a forensic approach countering SIFT keypoint removal by changing to a different type of keypoints in forensic analysis, clearly demonstrating benefits over traditional SIFT keypoint oriented techniques.

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Michele Alberti, Angela Botros, Schuetz Narayan, Rolf Ingold, Marcus Liwicki, Mathias Seuret

Auto-TLDR; Trainable and Spectrally Initializable Matrix Transformations for Neural Networks

Abstract Slides Poster Similar

In this work, we introduce a new architectural component to Neural Networks (NN), i.e., trainable and spectrally initializable matrix transformations on feature maps. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers) images to historical documents (CB55). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases appreciably across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.

Face Anti-Spoofing Using Spatial Pyramid Pooling

Lei Shi, Zhuo Zhou, Zhenhua Guo

Auto-TLDR; Spatial Pyramid Pooling for Face Anti-Spoofing

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Face recognition system is vulnerable to many kinds of presentation attacks, so how to effectively detect whether the image is from the real face is particularly important. At present, many deep learning-based anti-spoofing methods have been proposed. But these approaches have some limitations, for example, global average pooling (GAP) easily loses local information of faces, single-scale features easily ignore information differences in different scales, while a complex network is prune to be overfitting. In this paper, we propose a face anti-spoofing approach using spatial pyramid pooling (SPP). Firstly, we use ResNet-18 with a small amount of parameter as the basic model to avoid overfitting. Further, we use spatial pyramid pooling module in the single model to enhance local features while fusing multi-scale information. The effectiveness of the proposed method is evaluated on three databases, CASIA-FASD, Replay-Attack and CASIA-SURF. The experimental results show that the proposed approach can achieve state-of-the-art performance.

Efficient-Receptive Field Block with Group Spatial Attention Mechanism for Object Detection

Jiacheng Zhang, Zhicheng Zhao, Fei Su

Auto-TLDR; E-RFB: Efficient-Receptive Field Block for Deep Neural Network for Object Detection

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Object detection has been paid rising attention in computer vision field. Convolutional Neural Networks (CNNs) extract high-level semantic features of images, which directly determine the performance of object detection. As a common solution, embedding integration modules into CNNs can enrich extracted features and thereby improve the performance. However, the instability and inconsistency of internal multiple branches exist in these modules. To address this problem, we propose a novel multibranch module called Efficient-Receptive Field Block (E-RFB), in which multiple levels of features are combined for network optimization. Specifically, by downsampling and increasing depth, the E-RFB provides sufficient RF. Second, in order to eliminate the inconsistency across different branches, a novel spatial attention mechanism, namely, Group Spatial Attention Module (GSAM) is proposed. The GSAM gradually narrows a feature map by channel grouping; thus it encodes the information between spatial and channel dimensions into the final attention heat map. Third, the proposed module can be easily joined in various CNNs to enhance feature representation as a plug-and-play component. With SSD-style detectors, our method halves the parameters of the original detection head and achieves high accuracy on the PASCAL VOC and MS COCO datasets. Moreover, the proposed method achieves superior performance compared with state-of-the-art methods based on similar framework.

CQNN: Convolutional Quadratic Neural Networks

Pranav Mantini, Shishir Shah

Auto-TLDR; Quadratic Neural Network for Image Classification

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Image classification is a fundamental task in computer vision. A variety of deep learning models based on the Convolutional Neural Network (CNN) architecture have proven to be an efficient solution. Numerous improvements have been proposed over the years, where broader, deeper, and denser networks have been constructed. However, the atomic operation for these models has remained a linear unit (single neuron). In this work, we pursue an alternative dimension by hypothesizing the atomic operation to be performed by a quadratic unit. We construct convolutional layers using quadratic neurons for feature extraction and subsequently use dense layers for classification. We perform analysis to quantify the implication of replacing linear neurons with quadratic units. Results show a keen improvement in classification accuracy with quadratic neurons over linear neurons.

Video Face Manipulation Detection through Ensemble of CNNs

Nicolo Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, Stefano Tubaro

Auto-TLDR; Face Manipulation Detection in Video Sequences Using Convolutional Neural Networks

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In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in video sequences with incredibly realistic results and a very little effort. Despite the usefulness of these tools in many fields, if used maliciously, they can have a significantly bad impact on society (e.g., fake news spreading, cyber bullying through fake revenge porn). The ability of objectively detecting whether a face has been manipulated in a video sequence is then a task of utmost importance. In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques. In particular, we study the ensembling of different trained Convolutional Neural Network (CNN) models. In the proposed solution, different models are obtained starting from a base network (i.e., EfficientNetB4) making use of two different concepts: (i) attention layers; (ii) siamese training. We show that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.

Residual Fractal Network for Single Image Super Resolution by Widening and Deepening

Jiahang Gu, Zhaowei Qu, Xiaoru Wang, Jiawang Dan, Junwei Sun

Auto-TLDR; Residual fractal convolutional network for single image super-resolution

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The architecture of the convolutional neural network (CNN) plays an important role in single image super-resolution (SISR). However, most models proposed in recent years usually transplant methods or architectures that perform well in other vision fields. Thence they do not combine the characteristics of super-resolution (SR) and ignore the key information brought by the recurring texture feature in the image. To utilize patch-recurrence in SR and the high correlation of texture, we propose a residual fractal convolutional block (RFCB) and expand its depth and width to obtain residual fractal network (RFN), which contains deep residual fractal network (DRFN) and wide residual fractal network (WRFN). RFCB is recursive with multiple branches of magnified receptive field. Through the phased feature fusion module, the network focuses on extracting high-frequency texture feature that repeatedly appear in the image. We also introduce residual in residual (RIR) structure to RFCB that enables abundant low-frequency feature feed into deeper layers and reduce the difficulties of network training. RFN is the first supervised learning method to combine the patch-recurrence characteristic in SISR into network design. Extensive experiments demonstrate that RFN outperforms state-of-the-art SISR methods in terms of both quantitative metrics and visual quality, while the amount of parameters has been greatly optimized.

Documents Counterfeit Detection through a Deep Learning Approach

Darwin Danilo Saire Pilco, Salvatore Tabbone

Auto-TLDR; End-to-End Learning for Counterfeit Documents Detection using Deep Neural Network

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The main topic of this work is on the detection of counterfeit documents and especially banknotes. We propose an end-to-end learning model using a deep learning approach based on Adapnet++ which manages feature extraction at multiple scale levels using several residual units. Unlike previous models based on regions of interest (ROI) and high-resolution documents, our network is feed with simple input images (i.e., a single patch) and we do not need high resolution images. Besides, discriminative regions can be visualized at different scales. Our network learns by itself which regions of interest predict the better results. Experimental results show that we are competitive compared with the state-of-the-art and our deep neural network has good ability to generalize and can be applied to other kind of documents like identity or administrative one.

Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning

Hyunseung Chung, Woo-Jeoung Nam, Seong-Whan Lee

Auto-TLDR; Robust Remote Sensing Image Retrieval Using Group Convolution with Attention Mechanism and Metric Learning

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Remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations. For refinement and emphasis on important features, we applied channel attention in each group convolution stage. By utilizing the characteristics of group convolution and channel-wise attention, it is possible to acknowledge the equality among rotated but identically located images. The training procedure has two main steps: (i) training the network with Aerial Image Dataset (AID) for classification, (ii) fine-tuning the network with triplet-loss for retrieval with Google Earth South Korea and NWPU-RESISC45 datasets. Results show that the proposed method performance exceeds other state-of-the-art retrieval methods in both rotated and original environments. Furthermore, we utilize class activation maps (CAM) to visualize the distinct difference of main features between our method and baseline, resulting in better adaptability in rotated environments.

How Does DCNN Make Decisions?

Yi Lin, Namin Wang, Xiaoqing Ma, Ziwei Li, Gang Bai

Auto-TLDR; Exploring Deep Convolutional Neural Network's Decision-Making Interpretability

Abstract Slides Poster Similar

Deep Convolutional Neural Networks (DCNN), despite imitating the human visual system, present no such decision credibility as human observers. This phenomenon, therefore, leads to the limitations of DCNN's applications in the security and trusted computing, such as self-driving cars and medical diagnosis. Focusing on this issue, our work aims to explore the way DCNN makes decisions. In this paper, the major contributions we made are: firstly, provide the hypothesis, “point-wise activation” of convolution function, according to the analysis of DCNN’s architectures and training process; secondly, point out the effect of “point-wise activation” on DCNN’s uninterpretable classification and pool robustness, and then suggest, in particular, the contradiction between the traditional and DCNN’s convolution kernel functions; finally, distinguish decision-making interpretability from semantic interpretability, and indicate that DCNN’s decision-making mechanism need to evolve towards the direction of semantics in the future. Besides, the “point-wise activation” hypothesis and conclusions proposed in our paper are supported by extensive experimental results.

Computational Data Analysis for First Quantization Estimation on JPEG Double Compressed Images

Sebastiano Battiato, Oliver Giudice, Francesco Guarnera, Giovanni Puglisi

Auto-TLDR; Exploiting Discrete Cosine Transform Coefficients for Multimedia Forensics

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Multimedia Forensics experts work consists in providing answers about integrity of a specific media content and from where it comes from. Exploitation of any traces from JPEG double compressed images is often one of the main investigative path to be used for these purposes. Thus it is fundamental to have tools and algorithms able to safely estimate the first quantization matrix to further proceed with camera model identification and related tasks. In this paper, a technique based on extensive simulation is proposed, with the aim to infer the first quantization for a certain numbers of Discrete Cosine Transform (DCT) coefficients exploiting local image statistics without using any a-priori knowledge. The method provides also a reliable confidence value for the estimation which is of great importance for forensic purposes. Experimental results w.r.t. the state-of-the-art demonstrate the effectiveness of the proposed technique both in terms of precision and overall reliability.

Face Anti-Spoofing Based on Dynamic Color Texture Analysis Using Local Directional Number Pattern

Junwei Zhou, Ke Shu, Peng Liu, Jianwen Xiang, Shengwu Xiong

Auto-TLDR; LDN-TOP Representation followed by ProCRC Classification for Face Anti-Spoofing

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Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel "LDN-TOP representation followed by ProCRC classification" pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detailed appearance information resisting illumination variations and noises, which can influence the texture pattern distribution. To further capture motion information, we extend LDN to a spatial-temporal variant named local directional number pattern from three orthogonal planes (LDN-TOP). The multi-scale LDN-TOP capturing complete information is extracted from color images to generate the feature vector with powerful representation capacity. Finally, the feature vector is fed into the probabilistic collaborative representation based classifier (ProCRC) for face anti-spoofing. Our method is evaluated on three challenging public datasets, namely CASIA FASD, Replay-Attack database, and UVAD database using sequence-based evaluation protocol. The experimental results show that our method can achieve promising performance with 0.37% EER on CASIA and 5.73% HTER on UVAD. The performance on Replay-Attack database is also competitive.

Multi-Resolution Fusion and Multi-Scale Input Priors Based Crowd Counting

Usman Sajid, Wenchi Ma, Guanghui Wang

Auto-TLDR; Multi-resolution Fusion Based End-to-End Crowd Counting in Still Images

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Crowd counting in still images is a challenging problem in practice due to huge crowd-density variations, large perspective changes, severe occlusion, and variable lighting conditions. The state-of-the-art patch rescaling module (PRM) based approaches prove to be very effective in improving the crowd counting performance. However, the PRM module requires an additional and compromising crowd-density classification process. To address these issues and challenges, the paper proposes a new multi-resolution fusion based end-to-end crowd counting network. It employs three deep-layers based columns/branches, each catering the respective crowd-density scale. These columns regularly fuse (share) the information with each other. The network is divided into three phases with each phase containing one or more columns. Three input priors are introduced to serve as an efficient and effective alternative to the PRM module, without requiring any additional classification operations. Along with the final crowd count regression head, the network also contains three auxiliary crowd estimation regression heads, which are strategically placed at each phase end to boost the overall performance. Comprehensive experiments on three benchmark datasets demonstrate that the proposed approach outperforms all the state-of-the-art models under the RMSE evaluation metric. The proposed approach also has better generalization capability with the best results during the cross-dataset experiments.

Motion U-Net: Multi-Cue Encoder-Decoder Network for Motion Segmentation

Gani Rahmon, Filiz Bunyak, Kannappan Palaniappan

Auto-TLDR; Motion U-Net: A Deep Learning Framework for Robust Moving Object Detection under Challenging Conditions

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Detection of moving objects is a critical first step in many computer vision applications. Several algorithms for motion and change detection were proposed. However, many of these approaches lack the ability to handle challenging real-world scenarios. Recently, deep learning approaches started to produce impressive solutions to computer vision tasks, particularly for detection and segmentation. Many existing deep learning networks proposed for moving object detection rely only on spatial appearance cues. In this paper, we propose a novel multi-cue and multi-stream network, Motion U-Net (MU-Net), which integrates motion, change, and appearance cues using a deep learning framework for robust moving object detection under challenging conditions. The proposed network consists of a two-stream encoder module followed by feature concatenation and a decoder module. Motion and change cues are computed through our tensor-based motion estimation and a multi-modal background subtraction modules. The proposed system was tested and evaluated on the change detection challenge datasets (CDnet-2014) and compared to state-of-the-art methods. On CDnet-2014 dataset, our approach reaches an average overall F-measure of 0.9852 and outperforms all current state-of-the-art methods. The network was also tested on the unseen SBI-2015 dataset and produced promising results.

Automatic Semantic Segmentation of Structural Elements related to the Spinal Cord in the Lumbar Region by Using Convolutional Neural Networks

Jhon Jairo Sáenz Gamboa, Maria De La Iglesia-Vaya, Jon Ander Gómez

Auto-TLDR; Semantic Segmentation of Lumbar Spine Using Convolutional Neural Networks

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This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline.

Multi-Order Feature Statistical Model for Fine-Grained Visual Categorization

Qingtao Wang, Ke Zhang, Shaoli Huang, Lianbo Zhang, Jin Fan

Auto-TLDR; Multi-Order Feature Statistical Method for Fine-Grained Visual Categorization

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Fine-grained visual categorization aims to learn a robust image representation modeling subtle differences from similar categories. Existing methods in this field tackle the problem by designing complex frameworks, which produce high-level features by performing first-order or second-order pooling. Despite the impressive performance achieved by these strategies, the single-order networks only carry linear or non-linear information of the last convolutional layer, neglecting the fact that feature from different orders are mutually complementary. In this paper, we propose a Multi-Order Feature Statistical Method (MOFS), which learns fine-grained features characterizing multiple orders. Specifically, the MOFS consists of two sub-modules: (i) a first-order module modeling both mid-level and high-level features. (ii) a covariance feature statistical module capturing high-order features. By deploying these two sub-modules on the top of existing backbone networks, MOFS simultaneously captures multi-level of discrimative patters including local, global and co-related patters. We evaluate the proposed method on three challenging benchmarks, namely CUB-200-2011, Stanford Cars, and FGVC-Aircraft. Compared with state-of-the-art methods, experiments results exhibit superior performance in recognizing fine-grained objects

SFPN: Semantic Feature Pyramid Network for Object Detection

Yi Gan, Wei Xu, Jianbo Su

Auto-TLDR; SFPN: Semantic Feature Pyramid Network to Address Information Dilution Issue in FPN

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Feature Pyramid Network(FPN) employs a top-down path to enhance low level feature by utilizing high level feature.However, further improvement of detector is greatly hindered by the inner defect of FPN. The dilution issue in FPN is analyzed in this paper, and a new architecture named Semantic Feature Pyramid Network(SFPN) is introduced to address the information imbalance problem caused by information dilution. The proposed method consists of two simple and effective components: Semantic Pyramid Module(SPM) and Semantic Feature Fusion Module(SFFM). To compensate for the weaknesses of FPN, the semantic segmentation result is utilized as an extra information source in our architecture.By constructing a semantic pyramid based on the segmentation result and fusing it with FPN, feature maps at each level can obtain the necessary information without suffering from the dilution issue. The proposed architecture could be applied on many detectors, and non-negligible improvement could be achieved. Although this method is designed for object detection, other tasks such as instance segmentation can also largely benefit from it. The proposed method brings Faster R-CNN and Mask R-CNN with ResNet-50 as backbone both 1.8 AP improvements respectively. Furthermore, SFPN improves Cascade R-CNN with backbone ResNet-101 from 42.4 AP to 43.5 AP.

Object Detection Model Based on Scene-Level Region Proposal Self-Attention

Yu Quan, Zhixin Li, Canlong Zhang, Huifang Ma

Auto-TLDR; Exploiting Semantic Informations for Object Detection

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The improvement of object detection performance is mostly focused on the extraction of local information near the region of interest in the image, which results in detection performance in this area being unable to achieve the desired effect. First, a depth-wise separable convolution network(D_SCNet-127 R-CNN) is built on the backbone network. Considering the importance of scene and semantic informations for visual recognition, the feature map is sent into the branch of the semantic segmentation module, region proposal network module, and the region proposal self-attention module to build the network of scene-level and region proposal self-attention module. Second, a deep reinforcement learning was utilized to achieve accurate positioning of border regression, and the calculation speed of the whole model was improved through implementing a light-weight head network. This model can effectively solve the limitation of feature extraction in traditional object detection and obtain more comprehensive detailed features. The experimental verification on MSCOCO17, VOC12, and Cityscapes datasets shows that the proposed method has good validity and scalability.

Audio-Based Near-Duplicate Video Retrieval with Audio Similarity Learning

Pavlos Avgoustinakis, Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Andreas L. Symeonidis, Ioannis Kompatsiaris

Auto-TLDR; AuSiL: Audio Similarity Learning for Near-duplicate Video Retrieval

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In this work, we address the problem of audio-based near-duplicate video retrieval. We propose the Audio Similarity Learning (AuSiL) approach that effectively captures temporal patterns of audio similarity between video pairs. For the robust similarity calculation between two videos, we first extract representative audio-based video descriptors by leveraging transfer learning based on a Convolutional Neural Network (CNN) trained on a large scale dataset of audio events, and then we calculate the similarity matrix derived from the pairwise similarity of these descriptors. The similarity matrix is subsequently fed to a CNN network that captures the temporal structures existing within its content. We train our network following a triplet generation process and optimizing the triplet loss function. To evaluate the effectiveness of the proposed approach, we have manually annotated two publicly available video datasets based on the audio duplicity between their videos. The proposed approach achieves very competitive results compared to three state-of-the-art methods. Also, unlike the competing methods, it is very robust for the retrieval of audio duplicates generated with speed transformations.

FC-DCNN: A Densely Connected Neural Network for Stereo Estimation

Dominik Hirner, Friedrich Fraundorfer

Auto-TLDR; FC-DCNN: A Lightweight Network for Stereo Estimation

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We propose a novel lightweight network for stereo estimation. Our network consists of a fully-convolutional densely connected neural network (FC-DCNN) that computes matching costs between rectified image pairs. Our FC-DCNN method learns expressive features and performs some simple but effective post-processing steps. The densely connected layer structure connects the output of each layer to the input of each subsequent layer. This network structure in addition to getting rid of any fully-connected layers leads to a very lightweight network. The output of this network is used in order to calculate matching costs and create a cost-volume. Instead of using time and memory-inefficient cost-aggregation methods such as semi-global matching or conditional random fields in order to improve the result, we rely on filtering techniques, namely median filter and guided filter. By computing a left-right consistency check we get rid of inconsistent values. Afterwards we use a watershed foreground-background segmentation on the disparity image with removed inconsistencies. This mask is then used to refine the final prediction. We show that our method works well for both challenging indoor and outdoor scenes by evaluating it on the Middlebury, KITTI and ETH3D benchmarks respectively.

Which are the factors affecting the performance of audio surveillance systems?

Antonio Greco, Antonio Roberto, Alessia Saggese, Mario Vento

Auto-TLDR; Sound Event Recognition Using Convolutional Neural Networks and Visual Representations on MIVIA Audio Events

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Sound event recognition systems are rapidly becoming part of our life, since they can be profitably used in several vertical markets, ranging from audio security applications to scene classification and multi-modal analysis in social robotics. In the last years, a not negligible part of the scientific community started to apply Convolutional Neural Networks (CNNs) to image-based representations of the audio stream, due to their successful adoption in almost all the computer vision tasks. In this paper, we carry out a detailed benchmark of various widely used CNN architectures and visual representations on a popular dataset, namely the MIVIA Audio Events database. Our analysis is aimed at understanding how these factors affect the sound event recognition performance with a particular focus on the false positive rate, very relevant in audio surveillance solutions. In fact, although most of the proposed solutions achieve a high recognition rate, the capability of distinguishing the events-of-interest from the background is often not yet sufficient for real systems, and prevent its usage in real applications. Our comprehensive experimental analysis investigates this aspect and allows to identify useful design guidelines for increasing the specificity of sound event recognition systems.

Writer Identification Using Deep Neural Networks: Impact of Patch Size and Number of Patches

Akshay Punjabi, José Ramón Prieto Fontcuberta, Enrique Vidal

Auto-TLDR; Writer Recognition Using Deep Neural Networks for Handwritten Text Images

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Traditional approaches for the recognition or identification of the writer of a handwritten text image used to relay on heuristic knowledge about the shape and other features of the strokes of previously segmented characters. However, recent works have done significantly advances on the state of the art thanks to the use of various types of deep neural networks. In most of all of these works, text images are decomposed into patches, which are processed by the networks without any previous character or word segmentation. In this paper, we study how the way images are decomposed into patches impact recognition accuracy, using three publicly available datasets. The study also includes a simpler architecture where no patches are used at all - a single deep neural network inputs a whole text image and directly provides a writer recognition hypothesis. Results show that bigger patches generally lead to improved accuracy, achieving in one of the datasets a significant improvement over the best results reported so far.

Merged 1D-2D Deep Convolutional Neural Networks for Nerve Detection in Ultrasound Images

Mohammad Alkhatib, Adel Hafiane, Pierre Vieyres

Auto-TLDR; A Deep Neural Network for Deep Neural Networks to Detect Median Nerve in Ultrasound-Guided Regional Anesthesia

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Ultrasound-Guided Regional Anesthesia (UGRA) becomes a standard procedure in surgical operations and contributes to pain management. It offers the advantages of the targeted nerve detection and provides the visualization of regions of interest such as anatomical structures. However, nerve detection is one of the most challenging tasks that anesthetists can encounter in the UGRA procedure. A computer-aided system that can detect automatically the nerve region would facilitate the anesthetist's daily routine and allow them to concentrate more on the anesthetic delivery. In this paper, we propose a new method based on merging deep learning models from different data to detect the median nerve. The merged architecture consists of two branches, one being one dimensional (1D) convolutional neural networks (CNN) branch and another 2D CNN branch. The merged architecture aims to learn the high-level features from 1D handcrafted noise-robust features and 2D ultrasound images. The obtained results show the validity and high accuracy of the proposed approach and its robustness.

Attention Pyramid Module for Scene Recognition

Zhinan Qiao, Xiaohui Yuan, Chengyuan Zhuang, Abolfazl Meyarian

Auto-TLDR; Attention Pyramid Module for Multi-Scale Scene Recognition

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The unrestricted open vocabulary and diverse substances of scenery images bring significant challenges to scene recognition. However, most deep learning architectures and attention methods are developed on general-purpose datasets and omit the characteristics of scene data. In this paper, we exploit the attention pyramid module (APM) to tackle the predicament of scene recognition. Our method streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the interdependency among scales, and further assists feature re-calibration as well as aggregation process. APM is extremely light-weighted and can be easily plugged into existing network architectures in a parameter-efficient manner. By simply integrating APM into ResNet-50, we obtain a 3.54\% boost in terms of top-1 accuracy on the benchmark scene dataset. Comprehensive experiments show that APM achieves better performance comparing with state-of-the-art attention methods using significant less computation budget. Code and pre-trained models will be made publicly available.

A Novel Region of Interest Extraction Layer for Instance Segmentation

Leonardo Rossi, Akbar Karimi, Andrea Prati

Auto-TLDR; Generic RoI Extractor for Two-Stage Neural Network for Instance Segmentation

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Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extract a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome to the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP on bounding box detection and 1.7% AP on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection-groie

Triplet-Path Dilated Network for Detection and Segmentation of General Pathological Images

Jiaqi Luo, Zhicheng Zhao, Fei Su, Limei Guo

Auto-TLDR; Triplet-path Network for One-Stage Object Detection and Segmentation in Pathological Images

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Deep learning has been widely applied in the field of medical image processing. However, compared with flourishing visual tasks in natural images, the progress achieved in pathological images is not remarkable, and detection and segmentation, which are among basic tasks of computer vision, are regarded as two independent tasks. In this paper, we make full use of existing datasets and construct a triplet-path network using dilated convolutions to cooperatively accomplish one-stage object detection and nuclei segmentation for general pathological images. First, in order to meet the requirement of detection and segmentation, a novel structure called triplet feature generation (TFG) is designed to extract high-resolution and multiscale features, where features from different layers can be properly integrated. Second, considering that pathological datasets are usually small, a location-aware and partially truncated loss function is proposed to improve the classification accuracy of datasets with few images and widely varying targets. We compare the performance of both object detection and instance segmentation with state-of-the-art methods. Experimental results demonstrate the effectiveness and efficiency of the proposed network on two datasets collected from multiple organs.

Scene Text Detection with Selected Anchors

Anna Zhu, Hang Du, Shengwu Xiong

Auto-TLDR; AS-RPN: Anchor Selection-based Region Proposal Network for Scene Text Detection

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Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal network (AS-RPN) using effective selected anchors instead of dense anchors to extract text proposals. The center, scales, aspect ratios and orientations of anchors are learnable instead of fixing, which leads to high recall and greatly reduced numbers of anchors. By replacing the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve comparable performance with previous state-of-the-art text detecting approaches on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and MSRA-TD500 when using single-scale and single model (ResNet50) testing only.

Cross-View Relation Networks for Mammogram Mass Detection

Ma Jiechao, Xiang Li, Hongwei Li, Ruixuan Wang, Bjoern Menze, Wei-Shi Zheng

Auto-TLDR; Multi-view Modeling for Mass Detection in Mammogram

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In medical image analysis, multi-view modeling is crucial for pathology detection when the target lesion is presented in different views, e.g. mass lesions in breast. Currently mammogram is the most effective imaging modality for mass lesion detection of breast cancer at the early stage. The pathological information from the two paired views (i.e., medio-lateral oblique and cranio-caudal) are highly relational and complementary, which is crucial for diagnosis in clinical practice. Existing mass detection methods do not consider learning synergistic features from the two relational views. For the first time, we propose a novel mass detection framework to capture the latent relation information from the two paired views of a same mass in mammogram. We evaluate our model on a public mammogram dataset and a large-scale private dataset, demonstrating that the proposed method outperforms existing feature fusion approaches and state-of-the-art mass detection methods. We further analyze the performance gains from the relation modeling. Our quantitative and qualitative results suggest that jointly learning cross-view features boosts the detection performance of existing models, which is a promising avenue for mass detection task in mammogram.

Creating Classifier Ensembles through Meta-Heuristic Algorithms for Aerial Scene Classification

Álvaro Roberto Ferreira Jr., Gustavo Gustavo Henrique De Rosa, Joao Paulo Papa, Gustavo Carneiro, Fabio Augusto Faria

Auto-TLDR; Univariate Marginal Distribution Algorithm for Aerial Scene Classification Using Meta-Heuristic Optimization

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Aerial scene classification is a challenging task to be solved in the remote sensing area, whereas deep learning approaches, such as Convolutional Neural Networks (CNN), are being widely employed to overcome such a problem. Nevertheless, it is not straightforward to find single CNN models that can solve all aerial scene classification tasks, allowing the nurturing of a better alternative, which is to fuse CNN-based classifiers into an ensemble. However, an appropriate choice of the classifiers that will belong to the ensemble is a critical factor, as it is unfeasible to employ all the possible classifiers in the literature. Therefore, this work proposes a novel framework based on meta-heuristic optimization for creating optimized-ensembles in the context of aerial scene classification. The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios. Finally, one can observe that the Univariate Marginal Distribution Algorithm (UMDA) overcame popular literature meta-heuristic algorithms, such as Genetic Programming and Particle Swarm Optimization considering the adopted criteria in the performed experiments.

Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution

Xiaoyu Xiang, Qian Lin, Jan Allebach

Auto-TLDR; A Context-Aware Joint CAR and SR Neural Network for High-Resolution Text Recognition and Face Detection

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Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR neural network (CAJNN) that integrates both local and non-local features to solve CAR and SR in one-stage. Finally, a deep reconstruction network is adopted to predict high quality and high-resolution images. Evaluation on CAR and SR benchmark datasets shows that our CAJNN model outperforms previous methods and also takes 26.2% less runtime. Based on this model, we explore addressing two critical challenges in high-level computer vision: optical character recognition of low-resolution texts, and extremely tiny face detection. We demonstrate that CAJNN can serve as an effective image preprocessing method and improve the accuracy for real-scene text recognition (from 85.30% to 85.75%) and the average precision for tiny face detection (from 0.317 to 0.611).

Local Attention and Global Representation Collaborating for Fine-Grained Classification

He Zhang, Yunming Bai, Hui Zhang, Jing Liu, Xingguang Li, Zhaofeng He

Auto-TLDR; Weighted Region Network for Cosmetic Contact Lenses Detection

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The cosmetic contact lenses over an iris may change its original textural pattern that is the foundation for iris recognition, making the cosmetic lenses a possible and easy-to-use iris presentation attack means. Aiming at cosmetic contact lenses detection of practical application system, some approaches have been proposed but still facing unsolved problems, such as low quality iris images and inaccurate localized iris boundaries. In this paper, we propose a novel framework called Weighted Region Network (WRN) for the cosmetic contact lenses detection. The WRN includes both the local attention Weight Network and the global classification Region Network. With the inherent attention mechanism, the proposed network is able to find the most discriminative regions, which reduces the requirement for target detection and improves the ability of classification based on some specific areas and patterns. The Weight Network can be trained by using Rank loss and MSE loss without manual discriminative region annotations. Experiments are conducted on several databases and a new collected low-quality iris image database. The proposed method outperforms state-of-the-art fake iris detection algorithms, and is also effective for the fine-grained image classification task.

Attention-Based Deep Metric Learning for Near-Duplicate Video Retrieval

Kuan-Hsun Wang, Chia Chun Cheng, Yi-Ling Chen, Yale Song, Shang-Hong Lai

Auto-TLDR; Attention-based Deep Metric Learning for Near-duplicate Video Retrieval

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Near-duplicate video retrieval (NDVR) is an important and challenging problem due to the increasing amount of videos uploaded to the Internet. In this paper, we propose an attention-based deep metric learning method for NDVR. Our method is based on well-established principles: We leverage two-stream networks to combine RGB and optical flow features, and incorporate an attention module to effectively deal with distractor frames commonly observed in near duplicate videos. We further aggregate the features corresponding to multiple video segments to enhance the discriminative power. The whole system is trained using a deep metric learning objective with a Siamese architecture. Our experiments show that the attention module helps eliminate redundant and noisy frames, while focusing on visually relevant frames for solving NVDR. We evaluate our approach on recent large-scale NDVR datasets, CC_WEB_VIDEO, VCDB, FIVR and SVD. To demonstrate the generalization ability of our approach, we report results in both within- and cross-dataset settings, and show that the proposed method significantly outperforms state-of-the-art approaches.

Delving in the Loss Landscape to Embed Robust Watermarks into Neural Networks

Enzo Tartaglione, Marco Grangetto, Davide Cavagnino, Marco Botta

Auto-TLDR; Watermark Aware Training of Neural Networks

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In the last decade the use of artificial neural networks (ANNs) in many fields like image processing or speech recognition has become a common practice because of their effectiveness to solve complex tasks. However, in such a rush, very little attention has been paid to security aspects. In this work we explore the possibility to embed a watermark into the ANN parameters. We exploit model redundancy and adaptation capacity to lock a subset of its parameters to carry the watermark sequence. The watermark can be extracted in a simple way to claim copyright on models but can be very easily attacked with model fine-tuning. To tackle this culprit we devise a novel watermark aware training strategy. We aim at delving into the loss landscape to find an optimal configuration of the parameters such that we are robust to fine-tuning attacks towards the watermarked parameters. Our experimental results on classical ANN models trained on well-known MNIST and CIFAR-10 datasets show that the proposed approach makes the embedded watermark robust to fine-tuning and compression attacks.

Attention Based Coupled Framework for Road and Pothole Segmentation

Shaik Masihullah, Ritu Garg, Prerana Mukherjee, Anupama Ray

Auto-TLDR; Few Shot Learning for Road and Pothole Segmentation on KITTI and IDD

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In this paper, we propose a novel attention based coupled framework for road and pothole segmentation. In many developing countries as well as in rural areas, the drivable areas are neither well-defined, nor well-maintained. Under such circumstances, an Advance Driver Assistant System (ADAS) is needed to assess the drivable area and alert about the potholes ahead to ensure vehicle safety. Moreover, this information can also be used in structured environments for assessment and maintenance of road health. We demonstrate few shot learning approach for pothole detection to leverage accuracy even with fewer training samples. We report the exhaustive experimental results for road segmentation on KITTI and IDD datasets. We also present pothole segmentation on IDD.

Siamese Dynamic Mask Estimation Network for Fast Video Object Segmentation

Dexiang Hong, Guorong Li, Kai Xu, Li Su, Qingming Huang

Auto-TLDR; Siamese Dynamic Mask Estimation for Video Object Segmentation

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Video object segmentation(VOS) has been a fundamental topic in recent years, and many deep learning-based methods have achieved state-of-the-art performance on multiple benchmarks. However, most of these methods rely on pixel-level matching between the template and the searched frames on the whole image while the targets only occupy a small region. Calculating on the entire image brings lots of additional computation cost. Besides, the whole image may contain some distracting information resulting in many false-positive matching points. To address this issue, motivated by one-stage instance object segmentation methods, we propose an efficient siamese dynamic mask estimation network for fast video object segmentation. The VOS is decoupled into two tasks, i.e. mask feature learning and dynamic kernel prediction. The former is responsible for learning high-quality features to preserve structural geometric information, and the latter learns a dynamic kernel which is used to convolve with the mask feature to generate a mask output. We use Siamese neural network as a feature extractor and directly predict masks after correlation. In this way, we can avoid using pixel-level matching, making our framework more simple and efficient. Experiment results on DAVIS 2016 /2017 datasets show that our proposed methods can run at 35 frames per second on NVIDIA RTX TITAN while preserving competitive accuracy.

PSDNet: A Balanced Architecture of Accuracy and Parameters for Semantic Segmentation

Yue Liu, Zhichao Lian

Auto-TLDR; Pyramid Pooling Module with SE1Cblock and D2SUpsample Network (PSDNet)

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Abstract—In this paper, we present our Pyramid Pooling Module （PPM） with SE1Cblock and D2SUpsample Network (PSDNet), a novel architecture for accurate semantic segmentation. Started from the known work called Pyramid Scene Parsing Network (PSPNet), PSDNet takes advantage of pyramid pooling structure with channel attention module and feature transform module in Pyramid Pooling Module (PPM). The enhanced PPM with these two components can strengthen context information flowing in the network instead of damaging it. The channel attention module we mentioned is an improved “Squeeze and Excitation with 1D Convolution” (SE1C) block which can explicitly model interrelationship between channels with fewer number of parameters. We propose a feature transform module named “Depth to Space Upsampling” (D2SUpsample) in the PPM which keeps integrity of features by transforming features while interpolating features, at the same time reducing parameters. In addition, we introduce a joint strategy in SE1Cblock which combines two variants of global pooling without increasing parameters. Compared with PSPNet, our work achieves higher accuracy on public datasets with 73.97% mIoU and 82.89% mAcc accuracy on Cityscapes Dataset based on ResNet50 backbone.

SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection

Zhihua Li, Zheng Zhang, Lijun Yin

Auto-TLDR; Temporal Fusion and Self-Attention Network for Facial Action Unit Detection

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Research on facial action unit detection has shown remarkable performances by using deep spatial learning models in recent years, however, it is far from reaching its full capacity in learning due to the lack of use of temporal information of AUs across time. Since the AU occurrence in one frame is highly likely related to previous frames in a temporal sequence, exploring temporal correlation of AUs across frames becomes a key motivation of this work. In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a so-called SAT-Net) to address the AU detection problem. First of all, we input the deep features of a sequence into a convolutional LSTM network and fuse the previous temporal information into the feature map of the last frame, and continue to learn the AU occurrence. Second, considering the AU detection problem is a multi-label classification problem that individual label depends only on certain facial areas, we propose a new self-learned attention mask by focusing the detection of each AU on parts of facial areas through the learning of individual attention mask for each AU, thus increasing the AU independence without the loss of any spatial relations. Our extensive experiments show that the proposed framework achieves better results of AU detection over the state-of-the-arts on two benchmark databases (BP4D and DISFA).

On the Use of Benford's Law to Detect GAN-Generated Images

Nicolo Bonettini, Paolo Bestagini, Simone Milani, Stefano Tubaro

Auto-TLDR; Using Benford's Law to Detect GAN-generated Images from Natural Images

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The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford’s law to discriminate GAN-generated images from natural photographs. Benford’s law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose even in data scarcity scenarios where Convolutional Neural Network (CNN) architectures tend to fail.

Hierarchically Aggregated Residual Transformation for Single Image Super Resolution

Zejiang Hou, Sy Kung

Auto-TLDR; HARTnet: Hierarchically Aggregated Residual Transformation for Multi-Scale Super-resolution

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Visual patterns usually appear at different scales/sizes in natural images. Multi-scale feature representation is of great importance for the single-image super-resolution(SISR) task to reconstruct image objects at different scales.However, such characteristic has been rarely considered by CNN-based SISR methods. In this work, we propose a novel build-ing block, i.e. hierarchically aggregated residual transformation(HART), to achieve multi-scale feature representation in each layer of the network. Within each HART block, we connect multiple convolutions in a hierarchical residual-like manner, which greatly expands the range of effective receptive fields and helps to detect image features at different scales. To theoretically understand the proposed HART block, we recast SISR as an optimal control problem and show that HART effectively approximates the classical4th-order Runge-Kutta method, which has the merit of small local truncation error for solving numerical ordinary differential equation. By cascading the proposed HART blocks, we establish our high-performing HARTnet. Comparedwith existing SR state-of-the-arts (including those in NTIRE2019 SR Challenge leaderboard), the proposed HARTnet demonstrates consistent PSNR/SSIM performance improvements on various benchmark datasets under different degradation models.Moreover, HARTnet can efficiently restore more faithful high-resolution images than comparative SR methods (cf. Figure 1).

Detecting Manipulated Facial Videos: A Time Series Solution

Zhang Zhewei, Ma Can, Gao Meilin, Ding Bowen

Auto-TLDR; Face-Alignment Based Bi-LSTM for Fake Video Detection

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We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two different types of features: {Face-Alignment} and {Dense-Face-Alignment}, in which both of them are physiological signals that can be distinguished between fake and original videos. We choose 68 landmark points as the feature of {Face-Alignment} and Pose Adaptive Feature (PAF) for {Dense-Face-Alignment}. Based on these two facial features, we designed two deep networks. In addition, we optimize our network by adding an attention mechanism that improves detection precision. Our method is tested over benchmarks of Face Forensics/Face Forensics++ dataset and show a promising performance on inference speed while maintaining accuracy with state-of art solutions that deal against DeepFake.

Coarse to Fine: Progressive and Multi-Task Learning for Salient Object Detection

Dong-Goo Kang, Sangwoo Park, Joonki Paik

Auto-TLDR; Progressive and mutl-task learning scheme for salient object detection

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Most deep learning-based salient object detection (SOD) methods tried to manipulate the convolution block to effectively capture the context of object. In this paper, we propose a novel method, called progressive and mutl-task learning scheme, to extract the context of object by only manipulating the learning scheme without changing the network architecture. The progressive learning scheme is a method to grow the decoder progressively in the train phase. In other words, starting from easier low-resolution layers, it gradually adds high-resolution layers. Although the progressive learning successfullyl captures the context of object, its output boundary tends to be rough. To solve this problem, we also propose a multi-task learning (MTL) scheme that processes the object saliency map and contour in a single network jointly. The proposed MTL scheme trains the network in an edge-preserved direction through an auxiliary branch that learns contours. The proposed a learning scheme can be combined with other convolution block manipulation methods. Extensive experiments on five datasets show that the proposed method performs best compared with state-of-the-art methods in most cases.

Fine-Tuning Convolutional Neural Networks: A Comprehensive Guide and Benchmark Analysis for Glaucoma Screening

Amed Mvoulana, Rostom Kachouri, Mohamed Akil

Auto-TLDR; Fine-tuning Convolutional Neural Networks for Glaucoma Screening

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This work aimed at giving a comprehensive and in-detailed guide on the route to fine-tuning Convolutional Neural Networks (CNNs) for glaucoma screening. Transfer learning consists in a promising alternative to train CNNs from stratch, to avoid the huge data and resources requirements. After a thorough study of five state-of-the-art CNNs architectures, a complete and well-explained strategy for fine-tuning these networks is proposed, using hyperparameter grid-searching and two-phase training approach. Excellent performance is reached on model evaluation, with a 0.9772 AUROC validation rate, giving arise to reliable glaucoma diagosis-help systems. Also, a benchmark analysis is conducted across all fine-tuned models, studying them according to performance indices such as model complexity and size, AUROC density and inference time. This in-depth analysis allows a rigorous comparison between model characteristics, and is useful for giving practioners important trademarks for prospective applications and deployments.

Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

Martin Kolarik, Radim Burget, Carlos M. Travieso-Gonzalez, Jan Kocica

Auto-TLDR; Planar 3D Res-U-Net Network for Unbalanced 3D Image Segmentation using Fluid Attenuation Inversion Recover

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We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16 which is applied for a single-stage unbalanced 3D image data segmentation. In particular, we evaluate the method on the MICCAI 2016 MS lesion segmentation challenge dataset utilizing solely Fluid Attenuation Inversion Recover (FLAIR) sequence without brain extraction for training and inference to simulate real medical praxis. The planar 3D res-u-net network performed the best both in sensitivity and Dice score amongst end to end methods processing raw MRI scans and achieved comparable Dice score to a state-of-the-art unimodal not end to end approach. Complete source code was released under the open-source license and this paper is in compliance with the Machine learning Reproducibility Checklist. By implementing practical transfer learning for 3D data representation we were able to successfully segment heavily unbalanced data without selective sampling and achieved more reliable results using less training data in single modality. From medical perspective, the unimodal approach gives an advantage in real praxis as it does not require co-registration nor additional scanning time during examination. Although modern medical imaging methods capture high resolution 3D anatomy scans suitable for computer aided detection system processing, deployment of automatic systems for interpretation of radiology imaging is still rather theoretical in many medical areas. Our work aims to bridge the gap offering solution for partial research questions.

SCA Net: Sparse Channel Attention Module for Action Recognition

Hang Song, Yonghong Song, Yuanlin Zhang

Auto-TLDR; SCA Net: Efficient Group Convolution for Sparse Channel Attention

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Channel attention has shown its great performance recently when it was incorporated into deep convolutional neural networks. However, existing methods usually require extensive computing resources due to their involuted structure, which is hard for 3D CNNs to take full advantage of. In this paper, a lightweight sparse channel attention (SCA) module implemented by efficient group convolution is proposed, which adopts the idea of sparse channel connection and involves much less parameters but brings clear performance gain. Meanwhile, to solve the lack of local channel interaction brought by group convolution, a dominant function called Aggregate-Shuffle-Diverge (ASD) is leveraged to enhance information flow over each group with no additional parameters. We also adjust the existing mainstream 3D CNNs by employing 3D convolution factorization, so as to further reduce the parameters. Our SCA module can be flexibly incorporated into most existing 3D CNNs, all of which can achieve a perfect trade-off between performance and complexity on action recognition task with factorized I3D or 3D ResNet backbone networks. The experimental results also indicate that the resulting network, namely, SCA Net can achieve an outstanding performance on UCF-101 and HMDB-51 datasets.

Enhancing Semantic Segmentation of Aerial Images with Inhibitory Neurons

Ihsan Ullah, Sean Reilly, Michael Madden

Auto-TLDR; Lateral Inhibition in Deep Neural Networks for Object Recognition and Semantic Segmentation

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In a Convolutional Neural Network, each neuron in the output feature map takes input from the neurons in its receptive field. This receptive field concept plays a vital role in today's deep neural networks. However, inspired by neuro-biological research, it has been proposed to add inhibitory neurons outside the receptive field, which may enhance the performance of neural network models. In this paper, we begin with deep network architectures such as VGG and ResNet, and propose an approach to add lateral inhibition in each output neuron to reduce its impact on its neighbours, both in fine-tuning pre-trained models and training from scratch. Our experiments show that notable improvements upon prior baseline deep models can be achieved. A key feature of our approach is that it is easy to add to baseline models; it can be adopted in any model containing convolution layers, and we demonstrate its value in applications including object recognition and semantic segmentation of aerial images, where we show state-of-the-art result on the Aeroscape dataset. On semantic segmentation tasks, our enhancement shows 17.43% higher mIoU than a single baseline model on a single source (the Aeroscape dataset), 13.43% higher performance than an ensemble model on the same single source, and 7.03% higher than an ensemble model on multiple sources (segmentation datasets). Our experiments illustrate the potential impact of using inhibitory neurons in deep learning models, and they also show better results than the baseline models that have standard convolutional layer.

Progressive Learning Algorithm for Efficient Person Re-Identification

Zhen Li, Hanyang Shao, Liang Niu, Nian Xue

Auto-TLDR; Progressive Learning Algorithm for Large-Scale Person Re-Identification

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This paper studies the problem of Person Re-Identification (ReID) for large-scale applications. Recent research efforts have been devoted to building complicated part models, which introduce considerably high computational cost and memory consumption, inhibiting its practicability in large-scale applications. This paper aims to develop a novel learning strategy to find efficient feature embeddings while maintaining the balance of accuracy and model complexity. More specifically, we find by enhancing the classical triplet loss together with cross-entropy loss, our method can explore the hard examples and build a discriminant feature embedding yet compact enough for large-scale applications. Our method is carried out progressively using Bayesian optimization, and we call it the Progressive Learning Algorithm (PLA). Extensive experiments on three large-scale datasets show that our PLA is comparable or better than the state-of-the-arts. Especially, on the challenging Market-1501 dataset, we achieve Rank-1=94.7\%/mAP=89.4\% while saving at least 30\% parameters than strong part models.

Coarse-To-Fine Foreground Segmentation Based on Co-Occurrence Pixel-Block and Spatio-Temporal Attention Model

Xinyu Liu, Dong Liang

Auto-TLDR; Foreground Segmentation from coarse to Fine Using Co-occurrence Pixel-Block Model for Dynamic Scene

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Foreground segmentation in dynamic scene is an important task in video surveillance. The unsupervised background subtraction method based on background statistics modeling has difficulties in updating. On the other hand, the supervised foreground segmentation method based on deep learning relies on the large-scale of accurately annotated training data, which limits its cross-scene performance. In this paper, we propose a foreground segmentation method from coarse to fine. First, a across-scenes trained Spatio-Temporal Attention Model (STAM) is used to achieve coarse segmentation, which does not require training on specific scene. Then the coarse segmentation is used as a reference to help Co-occurrence Pixel-Block Model (CPB) complete the fine segmentation, and at the same time help CPB to update its background model. This method is more flexible than those deep-learning-based methods which depends on the specific-scene training, and realizes the accurate online dynamic update of the background model. Experimental results on WallFlower and LIMU validate our method outperforms STAM, CPB and other methods of participating in comparison.

MixNet for Generalized Face Presentation Attack Detection

Nilay Sanghvi, Sushant Singh, Akshay Agarwal, Mayank Vatsa, Richa Singh

Auto-TLDR; MixNet: A Deep Learning-based Network for Detection of Presentation Attacks in Cross-Database and Unseen Setting

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The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. Several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with the existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) fail miserably for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network called MixNet to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as Silicone Mask Attack Database (SMAD) and Spoof In the Wild with Multiple Attack (SiW-M). Extensive experiments and comparison with the existing state of the art algorithms show the effectiveness of the proposed algorithm.