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Mutually Guided Dual-Task Network for Scene Text Detection

Mengbiao Zhao, Wei Feng, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu

Auto-TLDR; A dual-task network for word-level and line-level text detection

Abstract Slides

Scene text detection has been studied extensively. Existing methods detect either words or text lines and use either word-level or line-level annotated data for training. In this paper, we propose a dual-task network that can perform word-level and line-level text detection simultaneously and use training data of both levels of annotation to boost the performance. The dual-task network has two detection heads for word-level and line-level text detection, respectively. Then we propose a mutual guidance scheme for the joint training of the two tasks with two modules: line filtering module utilizes the output of the text line detector to filter out the non-text regions for the word detector, and word enhancing module provides prior positions of words for the text line detector depending on the output of the word detector. Experimental results of word-level and line-level text detection demonstrate the effectiveness of the proposed dual-task network and mutual guidance scheme, and the results of our method are competitive with state-of-the-art methods.

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Feature Embedding Based Text Instance Grouping for Largely Spaced and Occluded Text Detection

Pan Gao, Qi Wan, Renwu Gao, Linlin Shen

Auto-TLDR; Text Instance Embedding Based Feature Embeddings for Multiple Text Instance Grouping

Abstract Slides Poster Similar

A text instance can be easily detected as multiple ones due to the large space between texts/characters, curved shape and partial occlusion. In this paper, a feature embedding based text instance grouping algorithm is proposed to solve this problem. To learn the feature space, a TIEM (Text Instance Embedding Module) is trained to minimize the within instance scatter and maximize the between instance scatter. Similarity between different text instances are measured in the feature space and merged if they meet certain conditions. Experimental results show that our approach can effectively connect text regions that belong to the same text instance. Competitive performance of our approach has been achieved on CTW1500, Total-Text, IC15 and a subset consists of texts selected from the three datasets, with large spacing and occlusions.

An Accurate Threshold Insensitive Kernel Detector for Arbitrary Shaped Text

Xijun Qian, Yifan Liu, Yu-Bin Yang

Auto-TLDR; TIKD: threshold insensitive kernel detector for arbitrary shaped text

Abstract Slides Similar

Recently, segmentation-based methods are popular in scene text detection due to the segmentation results can easily represent scene text of arbitrary shapes. However, previous works segment text instances the same as normal objects. It is obvious that the edge of the text instance differs from normal objects. In this paper, we propose a threshold insensitive kernel detector for arbitrary shaped text called TIKD, which includes a simple but stable base model and a new loss weight called Decay Loss Weight (DLW). By suppressing outlier pixels in a gradual way, the DLW can lead the network to detect more accurate text instances. Our method shows great power in accuracy and stability. It is worth mentioning that we achieve the precision, recall, f-measure of 88.7%, 83.7%, 86.1% respectively on the Total-Text dataset, with a fast speed of 16.3 frames per second. What’s more, even if we set the threshold in an extreme situation range from 0.1 to 0.9, our method can always achieve a stable f-measure over 79.9% on the Total-Text dataset.

TCATD: Text Contour Attention for Scene Text Detection

Ziling Hu, Wu Xingjiao, Jing Yang

Auto-TLDR; Text Contour Attention Text Detector

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Segmentation-based approaches have enabled state-of-the-art performance in long or curved text detection tasks. However, false detection still is a challenge when two text instances are close to each other. To address this problem, in this paper, we propose a Text Contour Attention Text Detector (TCATD), which can locate scene text with arbitrary orientation and shape accurately. Different from previous work, TCATD focus on text contour map (TC), text center intensity map (TCI) and text kernel maps (TK). The TC can introduce text contour information, the TCI can help to learn the accurate text segmentation and the TK can generate the complete shape of text instances. Besides, we propose a Text Contour Attention Module to deal with contour information. After the Text Contour Attention Module, TC, TCI and TK will be obtained. Extensive experiments on ICDAR2015, CTW1500 and Total-Text demonstrate that the proposed method achieves the state-of-the-art performance.

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.

RLST: A Reinforcement Learning Approach to Scene Text Detection Refinement

Xuan Peng, Zheng Huang, Kai Chen, Jie Guo, Weidong Qiu

Auto-TLDR; Saccadic Eye Movements and Peripheral Vision for Scene Text Detection using Reinforcement Learning

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Within the research of scene text detection, some previous work has already achieved significant accuracy and efficiency. However, most of the work was generally done without considering about the implicit relationship between detection and eye movements. In this paper, we propose a new method for scene text detection especially for its refinement based on reinforcement learning. The idea of this method is inspired by Saccadic Eye Movements and Peripheral Vision. A saccade makes it possible for humans to orient the gaze to the location where a visual object has appeared. Peripheral vision gathers visual information of surroundings which provides supplement to foveal vision during gazing. We propose a simple pipeline, imitating the way human eyes do a saccade and collect peripheral information, to locate scene text roughly and to refine multi-scale vision field iteratively using reinforcement learning. For both training and evaluation, we use ICDAR2015 Challenge 4 dataset as a base and design several criteria to measure the feasibility of our work.

DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents

Eun-Soo Jung, Hyeonggwan Son, Kyusam Oh, Yongkeun Yun, Soonhwan Kwon, Min Soo Kim

Auto-TLDR; Text Detection for Document Images Using Synthetic and Real Data

Abstract Slides Poster Similar

We present a novel approach to text detection for document images. For robust text detection of noisy scanned or captured document images, the advantages of multi-task learning are adopted by adding an auxiliary task of text enhancement. Consequently, our proposed model trains reducing noise and enhancing text regions as well as detecting text. To overcome the insufficiency of document image data for text detection, train data for our model are enriched with synthesized document images that are fully labeled for text detection and enhancement. For the effective use of synthetic and real data, the proposed model is trained in two phases. The first phase is training only synthetic data in a fully-supervised manner. Then real data with only detection labels are added in the second phase. The enhancement task for real data is weakly-supervised with information from detection labels. Our methods are demonstrated on a real document dataset with performances exceeding those of other methods. Also, we conducted ablations to analyze effects of the synthetic data, multi-task, and weak-supervision. Whereas the existing text detection studies mostly focus on the text in scenes, our proposed method is optimized to the applications for the text in scanned or captured documents.

Self-Training for Domain Adaptive Scene Text Detection

Yudi Chen, Wei Wang, Yu Zhou, Fei Yang, Dongbao Yang, Weiping Wang

Auto-TLDR; A self-training framework for image-based scene text detection

Abstract Slides Similar

Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. Then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. The simple Mask R-CNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.

A Multi-Head Self-Relation Network for Scene Text Recognition

Zhou Junwei, Hongchao Gao, Jiao Dai, Dongqin Liu, Jizhong Han

Auto-TLDR; Multi-head Self-relation Network for Scene Text Recognition

Abstract Slides Poster Similar

The text embedded in scene images can be seen everywhere in our lives. However, recognizing text from natural scene images is still a challenge because of its diverse shapes and distorted patterns. Recently, advanced recognition networks generally treat scene text recognition as a sequence prediction task. Although achieving excellent performance, these recognition networks consider the feature map cells as independent individuals and update cells state without utilizing the information of their neighboring cells. And the local receptive field of traditional convolutional neural network (CNN) makes a single cell that cannot cover the whole text region in an image. Due to these issues, the existing recognition networks cannot extract the global context in a visual scene. To deal with the above problems, we propose a Multi-head Self-relation Network(MSRN) for scene text recognition in this paper. The MSRN consists of several multi-head self-relation layers, which is designed for extracting the global context of a visual scene, so that transforms a cell into a new cell that fuses the information of the related cells. Furthermore, experiments over several public datasets demonstrate that our proposed recognition network achieves superior performance on several benchmark datasets including IC03, IC13, IC15, SVT-Perspective.

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

Abstract Slides Similar

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.

Gaussian Constrained Attention Network for Scene Text Recognition

Zhi Qiao, Xugong Qin, Yu Zhou, Fei Yang, Weiping Wang

Auto-TLDR; Gaussian Constrained Attention Network for Scene Text Recognition

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Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character area. In this paper, we propose Gaussian Constrained Attention Network to deal with this problem. It is a 2D attention-based method integrated with a novel Gaussian Constrained Refinement Module, which predicts an additional Gaussian mask to refine the attention weights. Different from adopting an additional supervision on the attention weights simply, our proposed method introduce an explicit refinement. In this way, the attention weights will be more concentrated and the attention-based recognition network achieves better performance. The proposed Gaussian Constrained Refinement Module is flexible and can be applied to existing attention-based methods directly. The experiments on several benchmark datasets demonstrate the effectiveness of our proposed method. Our code has been available at https://github.com/Pay20Y/GCAN.

Stratified Multi-Task Learning for Robust Spotting of Scene Texts

Kinjal Dasgupta, Sudip Das, Ujjwal Bhattacharya

Auto-TLDR; Feature Representation Block for Multi-task Learning of Scene Text

Abstract Slides Similar

Gaining control over the dynamics of multi-task learning should help to unlock the potential of the deep network to a great extent. In the existing multi-task learning (MTL) approaches of deep network, all the parameters of its feature encoding part are subjected to adjustments corresponding to each of the underlying sub-tasks. On the other hand, different functional areas of human brain are responsible for distinct functions such as the Broca's area of the cerebrum is responsible for speech formation whereas its Wernicke's area is related to the language development etc. Inspired by this fact, in the present study, we propose to introduce a block (termed as Feature Representation Block) of connection weights spanned over a few successive layers of a deep multi-task learning architecture and stratify the same into distinct subsets for their adjustments exclusively corresponding to different sub-tasks. Additionally, we have introduced a novel regularization component for controlled training of this Feature Representation Block. The purpose of the development of this learning framework is efficient end-to-end recognition of scene texts. Simulation results of the proposed strategy on various benchmark scene text datasets such as ICDAR 2015, ICDAR 2017 MLT, COCO-Text and MSRA-TD500 have improved respective SOTA performance.

Transferable Adversarial Attacks for Deep Scene Text Detection

Shudeng Wu, Tao Dai, Guanghao Meng, Bin Chen, Jian Lu, Shutao Xia

Auto-TLDR; Robustness of DNN-based STD methods against Adversarial Attacks

Abstract Slides Similar

Scene text detection (STD) aims to locate text in images and plays an important role in many computer vision tasks including automatic driving and text recognition systems. Recently, deep neural networks (DNNs) have been widely and successfully used in scene text detection, leading to plenty of DNN-based STD methods including regression-based and segmentation-based STD methods. However, recent studies have also shown that DNN is vulnerable to adversarial attacks, which can significantly degrade the performance of DNN models. In this paper, we investigate the robustness of DNN-based STD methods against adversarial attacks. To this end, we propose a generic and efficient attack method to generate adversarial examples, which are produced by adding small but imperceptible adversarial perturbation to the input images. Experiments on attacking four various models and a real-world STD engine of Google optical character recognition (OCR) show that the state-of-the-art DNN-based STD methods including regression-based and segmentation-based methods are vulnerable to adversarial attacks.

Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

Dayang Yu, Rong Zhang, Shan Qin

Auto-TLDR; Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

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Object detection in remote sensing images is a challenging task due to objects in the bird-view perspective appearing with arbitrary orientations. Though considerable progress has been made, there still exist challenges with the interference from complex backgrounds, dense arrangement, and large-scale variations. In this paper, we propose an oriented detector named Cascade Saliency Attention Network (CSAN), designed for comprehensively suppressing interference in remote sensing images. Specifically, we first combine context and pixel attention on feature maps to enhance saliency of objects for suppressing interference from backgrounds. Then, in cascade network, we apply instance segmentation on ROI to increase saliency of the central object, thus preventing object features from mutual interference in dense arrangement. Additionally, to alleviate large-scale variations, we devise a multi-scale merge module during FPN merging process to learn richer scale representations. Experimental results on DOTA and HRSC2016 datasets outperform other state-of-the-art object detection methods and verify the effectiveness of our method.

ReADS: A Rectified Attentional Double Supervised Network for Scene Text Recognition

Qi Song, Qianyi Jiang, Xiaolin Wei, Nan Li, Rui Zhang

Auto-TLDR; ReADS: Rectified Attentional Double Supervised Network for General Scene Text Recognition

Abstract Slides Poster Similar

In recent years, scene text recognition is always regarded as a sequence-to-sequence problem. Connectionist Temporal Classification (CTC) and Attentional sequence recognition (Attn) are two very prevailing approaches to tackle this problem while they may fail in some scenarios respectively. CTC concentrates more on every individual character but is weak in text semantic dependency modeling. Attn based methods have better context semantic modeling ability while tends to overfit on limited training data. In this paper, we elaborately design a Rectified Attentional Double Supervised Network (ReADS) for general scene text recognition. To overcome the weakness of CTC and Attn, both of them are applied in our method but with different modules in two supervised branches which can make a complementary to each other. Moreover, effective spatial and channel attention mechanisms are introduced to eliminate background noise and extract valid foreground information. Finally, a simple rectified network is implemented to rectify irregular text. The ReADS can be trained end-to-end and only word-level annotations are required. Extensive experiments on various benchmarks verify the effectiveness of ReADS which achieves state-of-the-art performance.

Multiple Document Datasets Pre-Training Improves Text Line Detection with Deep Neural Networks

Mélodie Boillet, Christopher Kermorvant, Thierry Paquet

Auto-TLDR; A fully convolutional network for document layout analysis

Abstract Slides Similar

In this paper, we introduce a fully convolutional network for the document layout analysis task. While state-of-the-art methods are using models pre-trained on natural scene images, our method relies on a U-shaped model trained from scratch for detecting objects from historical documents. We consider the line segmentation task and more generally the layout analysis problem as a pixel-wise classification task then our model outputs a pixel-labeling of the input images. We show that our method outperforms state-of-the-art methods on various datasets and also demonstrate that the pre-trained parts on natural scene images are not required to reach good results. In addition, we show that pre-training on multiple document datasets can improve the performances. We evaluate the models using various metrics to have a fair and complete comparison between the methods.

MagnifierNet: Learning Efficient Small-Scale Pedestrian Detector towards Multiple Dense Regions

Qi Cheng, Mingqin Chen, Yingjie Wu, Fei Chen, Shiping Lin

Auto-TLDR; MagnifierNet: A Simple but Effective Small-Scale Pedestrian Detection Towards Multiple Dense Regions

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Despite the success of pedestrian detection, there is still a significant gap in the performance of the detection of pedestrians at different scales. Detecting small-scale pedestrians is extremely challenging due to the low resolution of their convolution features which is essential for downstream classifiers. To address this issue, we observed pedestrian datasets and found that pedestrians often gather together in crowded public places. Then we propose MagnifierNet, a simple but effective small-scale pedestrian detector towards multiple dense regions. MagnifierNet uses our proposed sweep-line based grouping algorithm to find dense regions based on the number of pedestrians in the grouped region. And we adopt a new definition of small-scale pedestrians through grid search and KL-divergence. Besides, our grouping method can also be used as a new strategy for pedestrian data augmentation. The ablation study demonstrates that MagnifierNet improves the representation of small-scale pedestrians. We validate the effectiveness of MagnifierNet on CityPersons and KITTI datasets. Experimental results show that MagnifierNet achieves the best small-scale pedestrian detection performance on CityPersons benchmark without any external data, and also achieves competitive performance for detecting small-scale pedestrians on KITTI dataset without bells and whistles.

Hierarchical Head Design for Object Detectors

Shivang Agarwal, Frederic Jurie

Auto-TLDR; Hierarchical Anchor for SSD Detector

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The notion of anchor plays a major role in modern detection algorithms such as the Faster-RCNN or the SSD detector. Anchors relate the features of the last layers of the detector with bounding boxes containing objects in images. Despite their importance, the literature on object detection has not paid real attention to them. The motivation of this paper comes from the observations that (i) each anchor learns to classify and regress candidate objects independently (ii) insufficient examples are available for each anchor in case of small-scale datasets. This paper addresses these questions by proposing a novel hierarchical head for the SSD detector. The new design has the added advantage of no extra weights, as compared to the original design at inference time, while improving detectors performance for small size training sets. Improved performance on PASCAL-VOC and state-of-the-art performance on FlickrLogos-47 validate the method. We also show when the proposed design does not give additional performance gain over the original design.

Recognizing Multiple Text Sequences from an Image by Pure End-To-End Learning

Zhenlong Xu, Shuigeng Zhou, Fan Bai, Cheng Zhanzhan, Yi Niu, Shiliang Pu

Auto-TLDR; Pure End-to-End Learning for Multiple Text Sequences Recognition from Images

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We address a challenging problem: recognizing multiple text sequences from an image by pure end-to-end learning. It is twofold: 1) Multiple text sequences recognition. Each image may contain multiple text sequences of different content, location and orientation, we try to recognize all these texts in the image. 2) Pure end-to-end (PEE) learning.We solve the problem in a pure end-to-end learning way where each training image is labeled by only text transcripts of the contained sequences, without any geometric annotations. Most existing works recognize multiple text sequences from an image in a non-end-to-end (NEE) or quasi-end-to-end (QEE) way, in which each image is trained with both text transcripts and text locations. Only recently, a PEE method was proposed to recognize text sequences from an image where the text sequence was split to several lines in the image. However, it cannot be directly applied to recognizing multiple text sequences from an image. So in this paper, we propose a pure end-to-end learning method to recognize multiple text sequences from an image. Our method directly learns the probability distribution of multiple sequences conditioned on each input image, and outputs multiple text transcripts with a well-designed decoding strategy. To evaluate the proposed method, we construct several datasets mainly based on an existing public dataset and two real application scenarios. Experimental results show that the proposed method can effectively recognize multiple text sequences from images, and outperforms CTC-based and attention-based baseline methods.

MEAN: A Multi-Element Attention Based Network for Scene Text Recognition

Ruijie Yan, Liangrui Peng, Shanyu Xiao, Gang Yao, Jaesik Min

Auto-TLDR; Multi-element Attention Network for Scene Text Recognition

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Scene text recognition is a challenging problem due to the wide variances in content, style, orientation, and image quality of text instances in natural scene images. To learn the intrinsic representation of scene texts, a novel multi-element attention (MEA) mechanism is proposed to exploit geometric structures from local to global levels in the feature map extracted from a scene text image. The MEA mechanism is a generalized form of self-attention technique with the incorporation of graph structure modeling. The elements in feature maps are taken as the nodes of an undirected graph, and three kinds of adjacency matrices are introduced to aggregating information at local, neighborhood and global levels before calculating the attention weights. If only the local adjacency matrix is used, the MEA mechanism degenerates to a self-attention form. A multi-element attention network (MEAN) is implemented which includes a CNN for feature extraction, an encoder with MEA mechanism and a decoder for predicting text codes. Orientation positional encoding information is further added to the feature map output by the CNN, and a feature sequence as the encoder's input is obtained by element-level decomposition of the feature map. Experimental results show that MEAN has achieved state-of-the-art or competitive performance on public English scene text datasets. Further experiments and analyses conducted on both English and Chinese scene text datasets show that MEAN can handle horizontal, vertical, and irregular scene text samples.

Forground-Guided Vehicle Perception Framework

Kun Tian, Tong Zhou, Shiming Xiang, Chunhong Pan

Auto-TLDR; A foreground segmentation branch for vehicle detection

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As the basis of advanced visual tasks such as vehicle tracking and traffic flow analysis, vehicle detection needs to accurately predict the position and category of vehicle objects. In the past decade, deep learning based methods have made great progress. However, we also notice that some existing cases are not studied thoroughly. First, false positive on the background regions is one of the critical problems. Second, most of the previous approaches only optimize a single vehicle detection model, ignoring the relationship between different visual perception tasks. In response to the above two findings, we introduce a foreground segmentation branch for the first time, which can predict the pixel level of vehicles in advance. Furthermore, two attention modules are designed to guide the work of the detection branch. The proposed method can be easily grafted into the one-stage and two-stage detection framework. We evaluate the effectiveness of our model on LSVH, a dataset with large variations in vehicle scales, and achieve the state-of-the-art detection accuracy.

Weakly Supervised Attention Rectification for Scene Text Recognition

Chengyu Gu, Shilin Wang, Yiwei Zhu, Zheng Huang, Kai Chen

Auto-TLDR; An auxiliary supervision branch for attention-based scene text recognition

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Scene text recognition has become a hot topic in recent years due to its booming real-life applications. Attention-based encoder-decoder framework has become one of the most popular frameworks especially in the irregular text scenario. However, the “attention drift” problem reduces the recognition performance for most existing attention-based scene text recognition methods. To solve this problem, we propose an auxiliary supervision branch along with the attention-based encoder-decoder framework. A new loss function is designed to refine the feature map and to help the attention region align the target character area. Compared with existing attention rectification mechanisms, our method does not require character-level annotations or introduce any additional trainable parameter. Furthermore, our method can improve the performance for both RNN-Attention and Scaled Dot-Product Attention. The experiment results on various benchmarks have demonstrated that the proposed approach outperforms the state-of-the-art methods in both regular and irregular text recognition scenarios.

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.

IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes

Xiaoqian Li, Jie Liu, Shuwu Zhang

Auto-TLDR; IBN-STR: A Robust Text Recognition System Based on Data and Feature Representation

Abstract Poster Similar

Although text recognition methods based on deep neural networks have promising performance, there are still challenges due to the variety of text styles, perspective distortion, text with large curvature, and so on. To obtain a robust text recognizer, we have improved the performance from two aspects: data aspect and feature representation aspect. In terms of data, we transform the input images into S-shape distorted images in order to increase the diversity of training data. Besides, we explore the effects of different training data. In terms of feature representation, the combination of instance normalization and batch normalization improves the model's capacity and generalization ability. This paper proposes a robust text recognizer IBN-STR, which is an attention-based model. Through extensive experiments, the model analysis and comparison have been carried out from the aspects of data and feature representation, and the effectiveness of IBN-STR on both regular and irregular text instances has been verified. Furthermore, IBN-STR is an end-to-end recognition system that can achieve state-of-the-art performance.

Robust Lexicon-Free Confidence Prediction for Text Recognition

Qi Song, Qianyi Jiang, Rui Zhang, Xiaolin Wei

Auto-TLDR; Confidence Measurement for Optical Character Recognition using Single-Input Multi-Output Network

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Benefiting from the success of deep learning, Optical Character Recognition (OCR) is booming in recent years. As we all know, the text recognition results are vulnerable to slight perturbation in input images, thus a method for measuring how reliable the results are is crucial. In this paper, we present a novel method for confidence measurement given a text recognition result, which can be embedded in any text recognizer with little overheads. Our method consists of two stages with a coarse-to-fine style. The first stage generates multiple candidates for voting coarse scores by a Single-Input Multi-Output network (SIMO). The second stage calculates a refined confidence score referred by the voting result and the conditional probabilities of the Top-1 probable recognition sequence. Highly competitive performance is achieved on several standard benchmarks validates the efficiency and effectiveness of the proposed method. Moreover, it can be adopted in both Latin and non-Latin languages.

Detecting Objects with High Object Region Percentage

Fen Fang, Qianli Xu, Liyuan Li, Ying Gu, Joo-Hwee Lim

Auto-TLDR; Faster R-CNN for High-ORP Object Detection

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Object shape is a subtle but important factor for object detection. It has been observed that the object-region-percentage (ORP) can be utilized to improve detection accuracy for elongated objects, which have much lower ORPs than other types of objects. In this paper, we propose an approach to improve the detection performance for objects whose ORPs are relatively higher.To address the problem of high-ORP object detection, we propose a method consisting of three steps. First, we adjust the ground truth bounding boxes of high-ORP objects to an optimal range. Second, we train an object detector, Faster R-CNN, based on adjusted bounding boxes to achieve high recall. Finally, we train a DCNN to learn the adjustment ratios towards four directions and adjust detected bounding boxes of objects to get better localization for higher precision. We evaluate the effectiveness of our method on 12 high-ORP objects in COCO and 8 objects in a proprietary gearbox dataset. The experimental results show that our method can achieve state-of-the-art performance on these objects while costing less resources in training and inference stages.

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.

Learning a Dynamic High-Resolution Network for Multi-Scale Pedestrian Detection

Mengyuan Ding, Shanshan Zhang, Jian Yang

Auto-TLDR; Learningable Dynamic HRNet for Pedestrian Detection

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Pedestrian detection is a canonical instance of object detection in computer vision. In practice, scale variation is one of the key challenges, resulting in unbalanced performance across different scales. Recently, the High-Resolution Network (HRNet) has become popular because high-resolution feature representations are more friendly to small objects. However, when we apply HRNet for pedestrian detection, we observe that it improves for small pedestrians on one hand, but hurts the performance for larger ones on the other hand. To overcome this problem, we propose a learnable Dynamic HRNet (DHRNet) aiming to generate different network paths adaptive to different scales. Specifically, we construct a parallel multi-branch architecture and add a soft conditional gate module allowing for dynamic feature fusion. Both branches share all the same parameters except the soft gate module. Experimental results on CityPersons and Caltech benchmarks indicate that our proposed dynamic HRNet is more capable of dealing with pedestrians of various scales, and thus improves the performance across different scales consistently.

Semantic Segmentation Refinement Using Entropy and Boundary-guided Monte Carlo Sampling and Directed Regional Search

Zitang Sun, Sei-Ichiro Kamata, Ruojing Wang, Weili Chen

Auto-TLDR; Directed Region Search and Refinement for Semantic Segmentation

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Semantic segmentation requires both large receptive field and accurate spatial information. Despite existing methods based on fully convolutional network have greatly improved the accuracy, the prediction results still do not show satisfactory on small objects and boundary regions. We propose a refinement algorithm to improve the result generated by front network. Our method takes a modified U-shape network to generate both of segmentation mask and semantic boundary, which are used as inputs of refinement algorithm. We creatively introduce information entropy to represent the confidence of the neural network's prediction corresponding to each pixel. The information entropy combined with the semantic boundary can capture those unpredictable pixels with low-confidence through Monte Carlo sampling. Each selected pixel will be used as initial seeds for directed region search and refinement. Our purpose is to search the neighbor high-confidence regions according to the initial seeds. The re-labeling approach is based on high-confidence results. Particularly, different from general region growing methods, our method adopts a directed region search strategy based on gradient descent to find the high-confidence region effectively. Our method improves the performance both on Cityscapes and PASCAL VOC datasets. In the evaluation of segmentation accuracy of some small objects, our method surpasses most of state of the art methods.

Global-Local Attention Network for Semantic Segmentation in Aerial Images

Minglong Li, Lianlei Shan, Weiqiang Wang

Auto-TLDR; GLANet: Global-Local Attention Network for Semantic Segmentation

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Errors in semantic segmentation task could be classified into two types: large area misclassification and local inaccurate boundaries. Previously attention based methods capture rich global contextual information, this is beneficial to diminish the first type of error, but local imprecision still exists. In this paper we propose Global-Local Attention Network (GLANet) with a simultaneous consideration of global context and local details. Specifically, our GLANet is composed of two branches namely global attention branch and local attention branch, and three different modules are embedded in the two branches for the purpose of modeling semantic interdependencies in spatial, channel and boundary dimensions respectively. We sum the outputs of the two branches to further improve feature representation, leading to more precise segmentation results. The proposed method achieves very competitive segmentation accuracy on two public aerial image datasets, bringing significant improvements over baseline.

Text Recognition - Real World Data and Where to Find Them

Klára Janoušková, Lluis Gomez, Dimosthenis Karatzas, Jiri Matas

Auto-TLDR; Exploiting Weakly Annotated Images for Text Extraction

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We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The proposed method includes matching of imprecise transcription to weak annotations and edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as "pseudo ground truth" (PGT). We apply the method to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7 % on average, across different benchmark datasets (image domains) and 24.5 % on one of the weakly annotated datasets.

Construction Worker Hardhat-Wearing Detection Based on an Improved BiFPN

Chenyang Zhang, Zhiqiang Tian, Jingyi Song, Yaoyue Zheng, Bo Xu

Auto-TLDR; A One-Stage Object Detection Method for Hardhat-Wearing in Construction Site

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Work in the construction site is considered to be one of the occupations with the highest safety risk factor. Therefore, safety plays an important role in construction site. One of the most fundamental safety rules in construction site is to wear a hardhat. To strengthen the safety of the construction site, most of the current methods use multi-stage method for hardhat-wearing detection. These methods have limitations in terms of adaptability and generalizability. In this paper, we propose a one-stage object detection method based on convolutional neural network. We present a multi-scale strategy that selects the high-resolution feature maps of DarkNet-53 to effectively identify small-scale hardhats. In addition, we propose an improved weighted bi-directional feature pyramid network (BiFPN), which could fuse more semantic features from more scales. The proposed method can not only detect hardhat-wearing, but also identify the color of the hardhat. Experimental results show that the proposed method achieves a mAP of 87.04%, which outperforms several state-of-the-art methods on a public dataset.

Multiscale Attention-Based Prototypical Network for Few-Shot Semantic Segmentation

Yifei Zhang, Desire Sidibe, Olivier Morel, Fabrice Meriaudeau

Auto-TLDR; Few-shot Semantic Segmentation with Multiscale Feature Attention

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Deep learning-based image understanding techniques require a large number of labeled images for training. Few-shot semantic segmentation, on the contrary, aims at generalizing the segmentation ability of the model to new categories given only a few labeled samples. To tackle this problem, we propose a novel prototypical network (MAPnet) with multiscale feature attention. To fully exploit the representative features of target classes, we firstly extract rich contextual information of labeled support images via a multiscale feature enhancement module. The learned prototypes from support features provide further semantic guidance on the query image. Then we adaptively integrate multiple similarity-guided probability maps by attention mechanism, yielding an optimal pixel-wise prediction. Furthermore, the proposed method was validated on the PASCAL-5i dataset in terms of 1-way N-shot evaluation. We also test the model with weak annotations, including scribble and bounding box annotations. Both the qualitative and quantitative results demonstrate the advantages of our approach over other state-of-the-art methods.

Bidirectional Matrix Feature Pyramid Network for Object Detection

Wei Xu, Yi Gan, Jianbo Su

Auto-TLDR; BMFPN: Bidirectional Matrix Feature Pyramid Network for Object Detection

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Feature pyramids are widely used to improve scale invariance for object detection. Most methods just map the objects to feature maps with relevant square receptive fields, but rarely pay attention to the aspect ratio variation, which is also an important property of object instances. It will lead to a poor match between rectangular objects and assigned features with square receptive fields, thus preventing from accurate recognition and location. Besides, the information propagation among feature layers is sparse, namely, each feature in the pyramid may mainly or only contain single-level information, which is not representative enough for classification and localization sub-tasks. In this paper, Bidirectional Matrix Feature Pyramid Network (BMFPN) is proposed to address these issues. It consists of three modules: Diagonal Layer Generation Module (DLGM), Top-down Module (TDM) and Bottom-up Module (BUM). First, multi-level features extracted by backbone are fed into DLGM to produce the base features. Then these base features are utilized to construct the final feature pyramid through TDM and BUM in series. The receptive fields of the designed feature layers in BMFPN have various scales and aspect ratios. Objects can be correctly assigned to appropriate and representative feature maps with relevant receptive fields depending on its scale and aspect ratio properties. Moreover, TDM and BUM form bidirectional and reticular information flow, which effectively fuses multi level information in top-down and bottom-up manner respectively. To evaluate the effectiveness of our proposed architecture, an end-toend anchor-free detector is designed and trained by integrating BMFPN into FCOS. And the center ness branch in FCOS is modified with our Gaussian center-ness branch (GCB), which brings another slight improvement. Without bells and whistles, our method gains +3.3%, +2.4% and +2.6% AP on MS COCO dataset from baselines with ResNet-50, ResNet-101 and ResNeXt-101 backbones, respectively.

Vision-Based Layout Detection from Scientific Literature Using Recurrent Convolutional Neural Networks

Huichen Yang, William Hsu

Auto-TLDR; Transfer Learning for Scientific Literature Layout Detection Using Convolutional Neural Networks

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We present an approach for adapting convolutional neural networks for object recognition and classification to scientific literature layout detection (SLLD), a shared subtask of several information extraction problems. Scientific publications contain multiple types of information sought by researchers in various disciplines, organized into an abstract, bibliography, and sections documenting related work, experimental methods, and results; however, there is no effective way to extract this information due to their diverse layout. In this paper, we present a novel approach to developing an end-to-end learning framework to segment and classify major regions of a scientific document. We consider scientific document layout analysis as an object detection task over digital images, without any additional text features that need to be added into the network during the training process. Our technical objective is to implement transfer learning via fine-tuning of pre-trained networks and thereby demonstrate that this deep learning architecture is suitable for tasks that lack very large document corpora for training. As part of the experimental test bed for empirical evaluation of this approach, we created a merged multi-corpus data set for scientific publication layout detection tasks. Our results show good improvement with fine-tuning of a pre-trained base network using this merged data set, compared to the baseline convolutional neural network architecture.

Sample-Aware Data Augmentor for Scene Text Recognition

Guanghao Meng, Tao Dai, Shudeng Wu, Bin Chen, Jian Lu, Yong Jiang, Shutao Xia

Auto-TLDR; Sample-Aware Data Augmentation for Scene Text Recognition

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Deep neural networks (DNNs) have been widely used in scene text recognition, and achieved remarkable performance. Such DNN-based scene text recognizers usually require plenty of training data for training, but data collection and annotation is usually cost-expensive in practice. To alleviate this issue, data augmentation is often applied to train the scene text recognizers. However, existing data augmentation methods including affine transformation and elastic transformation methods suffer from the problems of under- and over-diversity, due to the complexity of text contents and shapes. In this paper, we propose a sample-aware data augmentor to transform samples adaptively based on the contents of samples. Specifically, our data augmentor consists of three parts: gated module, affine transformation module, and elastic transformation module. In our data augmentor, affine transformation module focuses on keeping the affinity of samples, while elastic transformation module aims to improve the diversity of samples. With the gated module, our data augmentor determines transformation type adaptively based on the properties of training samples and the recognizer capability during the training process. Besides, our framework introduces an adversarial learning strategy to optimize the augmentor and the recognizer jointly. Extensive experiments on scene text recognition benchmarks show that our sample-aware data augmentor significantly improves the performance of state-of-the-art scene text recognizer.

Tiny Object Detection in Aerial Images

Jinwang Wang, Wen Yang, Haowen Guo, Ruixiang Zhang, Gui-Song Xia

Auto-TLDR; Tiny Object Detection in Aerial Images Using Multiple Center Points Based Learning Network

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Object detection in Earth Vision has achieved great progress in recent years. However, tiny object detection in aerial images remains a very challenging problem since the tiny objects contain a small number of pixels and are easily confused with the background. To advance tiny object detection research in aerial images, we present a new dataset for Tiny Object Detection in Aerial Images (AI-TOD). Specifically, AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others. To build a benchmark for tiny object detection in aerial images, we evaluate the state-of-the-art object detectors on our AI-TOD dataset. Experimental results show that direct application of these approaches on AI-TOD produces suboptimal object detection results, thus new specialized detectors for tiny object detection need to be designed. Therefore, we propose a multiple center points based learning network (M-CenterNet) to improve the localization performance of tiny object detection, and experimental results show the significant performance gain over the competitors.

Local Gradient Difference Based Mass Features for Classification of 2D-3D Natural Scene Text Images

Lokesh Nandanwar, Shivakumara Palaiahnakote, Raghavendra Ramachandra, Tong Lu, Umapada Pal, Daniel Lopresti, Nor Badrul Anuar

Auto-TLDR; Classification of 2D and 3D Natural Scene Images Using COLD

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Methods developed for normal 2D text detection do not work well for a text that is rendered using decorative, 3D effects. This paper proposes a new method for classification of 2D and 3D natural scene images such that an appropriate method can be chosen or modified according to the complexity of the individual classes. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure we call COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed to a Neural Network (NN) for classification. The proposed method is tested on both a new dataset introduced in this work and a standard dataset assembled from different natural scene datasets, and compared to from existing methods to show its effectiveness. The approach improves text detection performance significantly after classification.

PRF-Ped: Multi-Scale Pedestrian Detector with Prior-Based Receptive Field

Yuzhi Tan, Hongxun Yao, Haoran Li, Xiusheng Lu, Haozhe Xie

Auto-TLDR; Bidirectional Feature Enhancement Module for Multi-Scale Pedestrian Detection

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Multi-scale feature representation is a common strategy to handle the scale variation in pedestrian detection. Existing methods simply utilize the convolutional pyramidal features for multi-scale representation. However, they rarely pay attention to the differences among different feature scales and extract multi-scale features from a single feature map, which may make the detectors sensitive to scale-variance in multi-scale pedestrian detection. In this paper, we introduce a bidirectional feature enhancement module (BFEM) to augment the semantic information of low-level features and the localization information of high-level features. In addition, we propose a prior-based receptive field block (PRFB) for multi-scale pedestrian feature extraction, where the receptive field is closer to the aspect ratio of the pedestrian target. Consequently, it is less affected by the surrounding background when extracting features. Experimental results indicate that the proposed method outperform the state-of-the-art methods on the CityPersons and Caltech datasets.

CASNet: Common Attribute Support Network for Image Instance and Panoptic Segmentation

Xiaolong Liu, Yuqing Hou, Anbang Yao, Yurong Chen, Keqiang Li

Auto-TLDR; Common Attribute Support Network for instance segmentation and panoptic segmentation

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Instance segmentation and panoptic segmentation is being paid more and more attention in recent years. In comparison with bounding box based object detection and semantic segmentation, instance segmentation can provide more analytical results at pixel level. Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes. CASNet is designed in the manner of fully convolutional and can implement training and inference from end to end. And CASNet manages predicting the instance without overlaps and holes, which problem exists in most of current instance segmentation algorithms. Furthermore, it can be easily extended to panoptic segmentation through minor modifications with little computation overhead. CASNet builds a bridge between semantic and instance segmentation from finding pixel class ID to obtaining class and instance ID by operations on common attribute. Through experiment for instance and panoptic segmentation, CASNet gets mAP 32.8\% and PQ 59.0\% on Cityscapes validation dataset by joint training, and mAP 36.3\% and PQ 66.1\% by separated training mode. For panoptic segmentation, CASNet gets state-of-the-art performance on the Cityscapes validation dataset.

Automated Whiteboard Lecture Video Summarization by Content Region Detection and Representation

Bhargava Urala Kota, Alexander Stone, Kenny Davila, Srirangaraj Setlur, Venu Govindaraju

Auto-TLDR; A Framework for Summarizing Whiteboard Lecture Videos Using Feature Representations of Handwritten Content Regions

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Lecture videos are rapidly becoming an invaluable source of information for students across the globe. Given the large number of online courses currently available, it is important to condense the information within these videos into a compact yet representative summary that can be used for search-based applications. We propose a framework to summarize whiteboard lecture videos by finding feature representations of detected handwritten content regions to determine unique content. We investigate multi-scale histogram of gradients and embeddings from deep metric learning for feature representation. We explicitly handle occluded, growing and disappearing handwritten content. Our method is capable of producing two kinds of lecture video summaries - the unique regions themselves or so-called key content and keyframes (which contain all unique content in a video segment). We use weighted spatio-temporal conflict minimization to segment the lecture and produce keyframes from detected regions and features. We evaluate both types of summaries and find that we obtain state-of-the-art peformance in terms of number of summary keyframes while our unique content recall and precision are comparable to state-of-the-art.

Nighttime Pedestrian Detection Based on Feature Attention and Transformation

Gang Li, Shanshan Zhang, Jian Yang

Auto-TLDR; FAM and FTM: Enhanced Feature Attention Module and Feature Transformation Module for nighttime pedestrian detection

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Pedestrian detection at nighttime is an important yet challenging task, which is fundamental for many practical applications, e.g. autonomous driving, video surveillance. To address this problem, in this work we start with some analysis, from which we find that the nighttime features have much more noise than that of daytime, resulting in low discrimination ability. Besides, we also observe some pedestrian examples are under adverse illumination conditions, and they can hardly provide sufficient information for accurate detection. Based on these findings, we propose the Feature Attention Module (FAM) and Feature Transformation Module (FTM) to enhance nighttime features. In FAM, guided by progressive segmentation supervision, hierarchical feature attention is produced to enhance multi-level features. On the other hand, FTM is introduced to enforce features from adverse illumination to approach that from better illumination. Based on feature attention and transformation (FAT) mechanism, a two-stage detector called FATNet is constructed for nighttime pedestrian detection. We conduct extensive experiments on nighttime datasets of EuroCity Persons (Night) and NightOwls to demonstrate the effectiveness of our method. On both two datasets, our method achieves significant improvements to the baseline and also outperforms state-of-the-art detectors.

A Fast and Accurate Object Detector for Handwritten Digit String Recognition

Jun Guo, Wenjing Wei, Yifeng Ma, Cong Peng

Auto-TLDR; ChipNet: An anchor-free object detector for handwritten digit string recognition

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Focusing on handwritten digit string recognition (HDSR), we propose an anchor-free object detector called ChipNet, where a novel encoding method is designed. The input image is divided into columns, and then these columns are encoded by the ground truth. The adjacent columns are responsible for detecting the same target so that it can well address the class-imbalanced problem meanwhile reducing the network computation. ChipNet is composed of convolutional and bidirectional long short term memory networks. Different from the typical detectors, it doesn't use region proposals, anchors or regions of interest pooling. Hence, it can overcome the shortages of anchor-based and dense detectors in HDSR. The experiments are implemented on the synthetic digit strings, the CVL HDS database, and the ORAND-CAR-A & B databases. The high accuracies, which surpass the reported results by a large margin (up to 6.62%), are achieved. Furthermore, it gets 219 FPS speed on 160*32 px resolution images when using a Tesla P100 GPU. The results also show that ChipNet can handle touching, connecting and arbitrary length digit strings, and the obtained accuracies in HDSR are as high as the ones in single handwritten digit recognition.

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.

The HisClima Database: Historical Weather Logs for Automatic Transcription and Information Extraction

Verónica Romero, Joan Andreu Sánchez

Auto-TLDR; Automatic Handwritten Text Recognition and Information Extraction from Historical Weather Logs

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Knowing the weather and atmospheric conditions from the past can help weather researchers to generate models like the ones used to predict how weather conditions are likely to change as global temperatures continue to rise. Many historical weather records are available from the past registered on a systemic basis. Historical weather logs were registered in ships, when they were on the high seas, recording daily weather conditions such as: wind speed, temperature, coordinates, etc. These historical documents represent an important source of knowledge with valuable information to extract climatic information of several centuries ago. Such information is usually collected by experts that devote a lot of time. This paper presents a new database, compiled from a ship log mainly composed by handwritten tables that contain mainly numerical information, to support research in automatic handwriting recognition and information extraction. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems and information extraction techniques, when applied to the presented database. Baseline results are reported for reference in future studies.

Text Recognition in Real Scenarios with a Few Labeled Samples

Jinghuang Lin, Cheng Zhanzhan, Fan Bai, Yi Niu, Shiliang Pu, Shuigeng Zhou

Auto-TLDR; Few-shot Adversarial Sequence Domain Adaptation for Scene Text Recognition

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Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications. Existing works mainly focus on learning a general model with a huge number of synthetic text images to recognize unconstrained scene texts, and have achieved substantial progress. However, these methods are not quite applicable in many real-world scenarios where 1) high recognition accuracy is required, while 2) labeled samples are lacked. To tackle this challenging problem, this paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation between the synthetic source domain (with many synthetic labeled samples) and a specific target domain (with only some or a few real labeled samples). This is done by simultaneously learning each character’s feature representation with an attention mech- anism and establishing the corresponding character-level latent subspace with adversarial learning. Our approach can maximize the character-level confusion between the source domain and the target domain, thus achieves the sequence-level adaptation with even a small number of labeled samples in the target domain. Extensive experiments on various datasets show that our method significantly outperforms the finetuning scheme, and obtains comparable performance to the state-of-the-art STR methods.

CenterRepp: Predict Central Representative Point Set's Distribution for Detection

Yulin He, Limeng Zhang, Wei Chen, Xin Luo, Chen Li, Xiaogang Jia

Auto-TLDR; CRPDet: CenterRepp Detector for Object Detection

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Object detection has long been an important issue in the discipline of scene understanding. Existing researches mainly focus on the object itself, ignoring its surrounding environment. In fact, the surrounding environment provides abundant information to help detectors classify and locate objects. This paper proposes CRPDet, viz. CenterRepp Detector, a framework for object detection. The main function of CRPDet is accomplished by the CenterRepp module, which takes into account the surrounding environment by predicting the distribution of the central representative points. CenterRepp converts labeled object frames into the mean and standard variance of the sampling points’ distribution. This helps increase the receptive field of objects, breaking the limitation of object frames. CenterRepp defines a position-fixed center point with significant weights, avoiding to sample all points in the surroundings. Experiments on the COCO test-dev detection benchmark demonstrates that our proposed CRPDet has comparable performance with state-of-the-art detectors, achieving 39.4 mAP with 51 FPS tested under single size input.

Small Object Detection by Generative and Discriminative Learning

Yi Gu, Jie Li, Chentao Wu, Weijia Jia, Jianping Chen

Auto-TLDR; Generative and Discriminative Learning for Small Object Detection

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With the development of deep convolutional neural networks (CNNs), the object detection accuracy has been greatly improved. But the performance of small object detection is still far from satisfactory, mainly because small objects are so tiny that the information contained in the feature map is limited. Existing methods focus on improving classification accuracy but still suffer from the limitation of bounding box prediction. To solve this issue, we propose a detection framework by generative and discriminative learning. First, a reconstruction generator network is designed to reconstruct the mapping from low frequency to high frequency for anchor box prediction. Then, a detector module extracts the regions of interest (ROIs) from generated results and implements a RoI-Head to predict object category and refine bounding box. In order to guide the reconstructed image related to the corresponding one, a discriminator module is adopted to tell from the generated result and the original image. Extensive evaluations on the challenging MS-COCO dataset demonstrate that our model outperforms most state-of-the-art models in detecting small objects, especially the reconstruction module improves the average precision for small object (APs) by 7.7%.

SyNet: An Ensemble Network for Object Detection in UAV Images

Berat Mert Albaba, Sedat Ozer

Auto-TLDR; SyNet: Combining Multi-Stage and Single-Stage Object Detection for Aerial Images

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Recent advances in camera equipped drone applications and their widespread use increased the demand on vision based object detection algorithms for aerial images. Object detection process is inherently a challenging task as a generic computer vision problem, however, since the use of object detection algorithms on UAVs (or on drones) is relatively a new area, it remains as a more challenging problem to detect objects in aerial images. There are several reasons for that including: (i) the lack of large drone datasets including large object variance, (ii) the large orientation and scale variance in drone images when compared to the ground images, and (iii) the difference in texture and shape features between the ground and the aerial images. Deep learning based object detection algorithms can be classified under two main categories: (a) single-stage detectors and (b) multi-stage detectors. Both single-stage and multi-stage solutions have their advantages and disadvantages over each other. However, a technique to combine the good sides of each of those solutions could yield even a stronger solution than each of those solutions individually. In this paper, we propose an ensemble network, SyNet, that combines a multi-stage method with a single-stage one with the motivation of decreasing the high false negative rate of multi-stage detectors and increasing the quality of the single-stage detector proposals. As building blocks, CenterNet and Cascade R-CNN with pretrained feature extractors are utilized along with an ensembling strategy. We report the state of the art results obtained by our proposed solution on two different datasets: namely MS-COCO and visDrone with \%52.1 $mAP_{IoU = 0.75}$ is obtained on MS-COCO $val2017$ dataset and \%26.2 $mAP_{IoU = 0.75}$ is obtained on VisDrone $test-set$. Our code is available at: https://github.com/mertalbaba/SyNet}{https://github.com/mer talbaba/SyNet