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IFSM: An Iterative Feature Selection Mechanism for Few-Shot Image Classification

Chunhao Cai, Minglei Yuan, Tong Lu

Auto-TLDR; Iterative Feature Selection Mechanism for Few-Shot Learning

Abstract Slides Poster

Nowadays many deep learning algorithms have been employed to solve different types of multimedia problems; however, most of them require a great amount of training data and tend to struggle in few-shot learning tasks. On the other hand, those methods designed for few-shot learning usually face the difficulty that once one or more samples show a relatively large bias, the predicted result may be much less reliable due to the fact that the sample will cause a large shift of class-level features during few-shot learning. To solve this problem, this paper presents a novel and Iterative Feature Selection Mechanism (IFSM) for few-shot image classification, which can be applied to lots of metric-based few-shot learners. IFSM learns to construct a more feasible class-level feature which is less affected by samples with relatively large biases, using an iterative approach. The proposed mechanism is tested on three previous state-of-the-art few-shot learning methods, and the experimental results show that the proposed mechanism considerably improves (by 1% to 2%) the image classification accuracies of both methods on the miniImageNet, tieredImageNet or CUB benchmarks in 5-way 5-shot tasks. This approves the effectiveness and generality of the proposed mechanism.

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TAAN: Task-Aware Attention Network for Few-Shot Classification

Zhe Wang, Li Liu, Fanzhang Li

Auto-TLDR; TAAN: Task-Aware Attention Network for Few-Shot Classification

Abstract Slides Poster Similar

Few-shot classification aims to recognize unlabeled samples from unseen classes given only a few labeled samples.Current approaches of few-shot learning usually employ a metriclearning framework to learn a feature similarity comparison between a query (test) example and the few support (training) examples. However, these approaches all extract features from samples independently without looking at the entire task as a whole, and so fail to provide an enough discrimination to features. Moreover, the existing approaches lack the ability to select the most relevant features for the task at hand. In this work, we propose a novel algorithm called Task-Aware Attention Network (TAAN) to address the above problems in few-shot classification. By inserting a Task-Relevant Channel Attention Module into metric-based few-shot learners, TAAN generates channel attentions for each sample by aggregating the context of the entire support set and identifies the most relevant features for similarity comparison. The experiment demonstrates that TAAN is competitive in overall performance comparing to the recent state-of-the-art systems and improves the performance considerably over baseline systems on both mini-ImageNet and tiered-ImageNet benchmarks.

MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization

Yangbin Chen, Yun Ma, Tom Ko, Jianping Wang, Qing Li

Auto-TLDR; MetaMix: A Meta-Agnostic Meta-Learning Algorithm for Few-Shot Classification

Abstract Slides Poster Similar

Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known as episodes) such that the initialized model can adapt quickly to new tasks. However, within each episode, current MAML-based algorithms have limitations in forming generalizable decision boundaries using only a few training examples. In this paper, we propose an approach called MetaMix. It generates virtual examples within each episode to regularize the backbone models. MetaMix can be applied in any of the MAML-based algorithms and learn the decision boundaries which are more generalizable to new tasks. Experiments on the mini-ImageNet, CUB, and FC100 datasets show that MetaMix improves the performance of MAML-based algorithms and achieves the state-of-the-art result when applied in Meta-Transfer Learning.

Complementing Representation Deficiency in Few-Shot Image Classification: A Meta-Learning Approach

Xian Zhong, Cheng Gu, Wenxin Huang, Lin Li, Shuqin Chen, Chia-Wen Lin

Auto-TLDR; Meta-learning with Complementary Representations Network for Few-Shot Learning

Abstract Slides Poster Similar

Few-shot learning is a challenging problem that has attracted more and more attention recently since abundant training samples are difficult to obtain in practical applications. Meta-learning has been proposed to address this issue, which focuses on quickly adapting a predictor as a base-learner to new tasks, given limited labeled samples. However, a critical challenge for meta-learning is the representation deficiency since it is hard to discover common information from a small number of training samples or even one, as is the representation of key features from such little information. As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks. Existing methods mostly resort to extracting less expressive features so as to avoid the representation deficiency. Aiming at learning better representations, we propose a meta-learning approach with complemented representations network (MCRNet) for few-shot image classification. In particular, we embed a latent space, where latent codes are reconstructed with extra representation information to complement the representation deficiency. Furthermore, the latent space is established with variational inference, collaborating well with different base-learners, and can be extended to other models. Finally, our end-to-end framework achieves the state-of-the-art performance in image classification on three standard few-shot learning datasets.

Meta Generalized Network for Few-Shot Classification

Wei Wu, Shanmin Pang, Zhiqiang Tian, Yaochen Li

Auto-TLDR; Meta Generalized Network for Few-Shot Classification

Abstract Similar

Few-shot classification aims to learn a well performance model with very limited labeled examples. There are mainly two directions for this aim, namely, meta- and metric-learning. Meta learning trains models in a particular way to fast adapt to new tasks, but it neglects variational features of images. Metric learning considers relationships among same or different classes, however on the downside, it usually fails to achieve competitive performance on unseen boundary examples. In this paper, we propose a Meta Generalized Network (MGNet) that aims to combine advantages of both meta- and metric-learning. There are two novel components in MGNet. Specifically, we first develop a meta backbone training method that both learns a flexible feature extractor and a classifier initializer efficiently, delightedly leading to fast adaption to unseen few-shot tasks without overfitting. Second, we design a trainable adaptive interval model to improve the cosine classifier, which increases the recognition accuracy of hard examples. We train the meta backbone in the training stage by all classes, and fine-tune the meta-backbone as well as train the adaptive classifier in the testing stage.

Local Propagation for Few-Shot Learning

Yann Lifchitz, Yannis Avrithis, Sylvaine Picard

Auto-TLDR; Local Propagation for Few-Shot Inference

Abstract Slides Poster Similar

The challenge in few-shot learning is that available data is not enough to capture the underlying distribution. To mitigate this, two emerging directions are (a) using local image representations, essentially multiplying the amount of data by a constant factor, and (b) using more unlabeled data, for instance by transductive inference, jointly on a number of queries. In this work, we bring these two ideas together, introducing local propagation. We treat local image features as independent examples, we build a graph on them and we use it to propagate both the features themselves and the labels, known and unknown. Interestingly, since there is a number of features per image, even a single query gives rise to transductive inference. As a result, we provide a universally safe choice for few-shot inference under both non-transductive and transductive settings, improving accuracy over corresponding methods. This is in contrast to existing solutions, where one needs to choose the method depending on the quantity of available data.

Graph-Based Interpolation of Feature Vectors for Accurate Few-Shot Classification

Yuqing Hu, Vincent Gripon, Stéphane Pateux

Auto-TLDR; Transductive Learning for Few-Shot Classification using Graph Neural Networks

Abstract Slides Poster Similar

In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples. In this context, works have proposed to introduce Graph Neural Networks (GNNs) aiming at exploiting the information contained in other samples treated concurrently, what is commonly referred to as the transductive setting in the literature. These GNNs are trained all together with a backbone feature extractor. In this paper, we propose a new method that relies on graphs only to interpolate feature vectors instead, resulting in a transductive learning setting with no additional parameters to train. Our proposed method thus exploits two levels of information: a) transfer features obtained on generic datasets, b) transductive information obtained from other samples to be classified. Using standard few-shot vision classification datasets, we demonstrate its ability to bring significant gains compared to other works.

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

Abstract Slides Similar

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.

Augmented Bi-Path Network for Few-Shot Learning

Baoming Yan, Chen Zhou, Bo Zhao, Kan Guo, Yang Jiang, Xiaobo Li, Zhang Ming, Yizhou Wang

Auto-TLDR; Augmented Bi-path Network for Few-shot Learning

Abstract Slides Poster Similar

Few-shot Learning (FSL) which aims to learn from few labeled training data is becoming a popular research topic, due to the expensive labeling cost in many real-world applications. One kind of successful FSL method learns to compare the testing (query) image and training (support) image by simply concatenating the features of two images and feeding it into the neural network. However, with few labeled data in each class, the neural network has difficulty in learning or comparing the local features of two images. Such simple image-level comparison may cause serious mis-classification. To solve this problem, we propose Augmented Bi-path Network (ABNet) for learning to compare both global and local features on multi-scales. Specifically, the salient patches are extracted and embedded as the local features for every image. Then, the model learns to augment the features for better robustness. Finally, the model learns to compare global and local features separately, \emph{i.e.}, in two paths, before merging the similarities. Extensive experiments show that the proposed ABNet outperforms the state-of-the-art methods. Both quantitative and visual ablation studies are provided to verify that the proposed modules lead to more precise comparison results.

Few-Shot Learning Based on Metric Learning Using Class Augmentation

Susumu Matsumi, Keiichi Yamada

Auto-TLDR; Metric Learning for Few-shot Learning

Abstract Slides Poster Similar

Few-shot learning is a machine learning problem in which new categories are learned from only a few samples. One approach for few-shot learning is metric learning, which learns an embedding space in which learning is efficient for few-shot samples. In this paper, we focus on metric learning and demonstrate that the number of classes in the training data used for metric learning has a greater impact on the accuracy of few-shot learning than the number of samples per class. We propose a few-shot learning approach based on metric learning in which the number of classes in the training data for performing metric learning is increased. The number of classes is augmented by synthesizing samples of imaginary classes at a feature level from the original training data. The proposed method is evaluated on the miniImageNet dataset using the nearest neighbor method or a support vector machine as the classifier, and the effectiveness of the approach is demonstrated.

Explanation-Guided Training for Cross-Domain Few-Shot Classification

Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, Alexander Binder

Auto-TLDR; Explaination-Guided Training for Cross-Domain Few-Shot Classification

Abstract Slides Poster Similar

Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces challenges originating from the limited labeled data in each class and, additionally, from the domain shift between training and test sets. In this paper, we introduce a novel training approach for existing FSC models. It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models. Firstly, we tailor the layer-wise relevance propagation (LRP) method to explain the prediction outcomes of FSC models. Secondly, we develop a model-agnostic explanation-guided training strategy that dynamically finds and emphasizes the features which are important for the predictions. Our contribution does not target a novel explanation method but lies in a novel application of explanations for the training phase. We show that explanation-guided training effectively improves the model generalization. We observe improved accuracy for three different FSC models: RelationNet, cross attention network, and a graph neural network-based formulation, on five few-shot learning datasets: miniImagenet, CUB, Cars, Places, and Plantae.

Few-Shot Few-Shot Learning and the Role of Spatial Attention

Yann Lifchitz, Yannis Avrithis, Sylvaine Picard

Auto-TLDR; Few-shot Learning with Pre-trained Classifier on Large-Scale Datasets

Abstract Slides Poster Similar

Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representation is learned on a limited amount of base class data, ignoring the amount of prior knowledge that a human may have accumulated before learning new tasks. At the same time, even if a powerful representation is available, it may happen in some domain that base class data are limited or non-existent. This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch. We adapt the representation in two stages, namely on the few base class data if available and on the even fewer data of new tasks. In doing so, we obtain from the pre-trained classifier a spatial attention map that allows focusing on objects and suppressing background clutter. This is important in the new problem, because when base class data are few, the network cannot learn where to focus implicitly. We also show that a pre-trained network may be easily adapted to novel classes, without meta-learning.

Incorporating Depth Information into Few-Shot Semantic Segmentation

Yifei Zhang, Desire Sidibe, Olivier Morel, Fabrice Meriaudeau

Auto-TLDR; RDNet: A Deep Neural Network for Few-shot Segmentation Using Depth Information

Abstract Slides Poster Similar

Few-shot segmentation presents a significant challenge for semantic scene understanding under limited supervision. Namely, this task targets at generalizing the segmentation ability of the model to new categories given a few samples. In order to obtain complete scene information, we extend the RGB-centric methods to take advantage of complementary depth information. In this paper, we propose a two-stream deep neural network based on metric learning. Our method, known as RDNet, learns class-specific prototype representations within RGB and depth embedding spaces, respectively. The learned prototypes provide effective semantic guidance on the corresponding RGB and depth query image, leading to more accurate performance. Moreover, we build a novel outdoor scene dataset, known as Cityscapes-3i, using labeled RGB images and depth images from the Cityscapes dataset. We also perform ablation studies to explore the effective use of depth information in few-shot segmentation tasks. Experiments on Cityscapes-3i show that our method achieves promising results with visual and complementary geometric cues from only a few labeled examples.

Task-based Focal Loss for Adversarially Robust Meta-Learning

Yufan Hou, Lixin Zou, Weidong Liu

Auto-TLDR; Task-based Adversarial Focal Loss for Few-shot Meta-Learner

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Adversarial robustness of machine learning has been widely studied in recent years, and a series of effective methods are proposed to resist adversarial attacks. However, less attention is paid to few-shot meta-learners which are much more vulnerable due to the lack of training samples. In this paper, we propose Task-based Adversarial Focal Loss (TAFL) to handle this tough challenge on a typical meta-learner called MAML. More concretely, we regard few-shot classification tasks as normal samples in learning models and apply focal loss mechanism on them. Our proposed method focuses more on adversarially fragile tasks, leading to improvement on overall model robustness. Results of extensive experiments on several benchmarks demonstrate that TAFL can effectively promote the performance of the meta-learner on adversarial examples with elaborately designed perturbations.

Pose-Robust Face Recognition by Deep Meta Capsule Network-Based Equivariant Embedding

Fangyu Wu, Jeremy Simon Smith, Wenjin Lu, Bailing Zhang

Auto-TLDR; Deep Meta Capsule Network-based Equivariant Embedding Model for Pose-Robust Face Recognition

Abstract Similar

Despite the exceptional success in face recognition related technologies, handling large pose variations still remains a key challenge. Current techniques for pose-robust face recognition either, directly extract pose-invariant features, or first synthesize a face that matches the target pose before feature extraction. It is more desirable to learn face representations equivariant to pose variations. To this end, this paper proposes a deep meta Capsule network-based Equivariant Embedding Model (DM-CEEM) with three distinct novelties. First, the proposed RB-CapsNet allows DM-CEEM to learn an equivariant embedding for pose variations and achieve the desired transformation for input face images. Second, we introduce a new version of a Capsule network called RB-CapsNet to extend CapsNet to perform a profile-to-frontal face transformation in deep feature space. Third, we train the DM-CEEM in a meta way by treating a single overall classification target as multiple sub-tasks that satisfy certain unknown probabilities. In each sub-task, we sample the support and query sets randomly. The experimental results on both controlled and in-the-wild databases demonstrate the superiority of DM-CEEM over state-of-the-art.

A Self-Supervised GAN for Unsupervised Few-Shot Object Recognition

Khoi Nguyen, Sinisa Todorovic

Auto-TLDR; Self-supervised Few-Shot Object Recognition with a Triplet GAN

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This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled, and test images are divided into queries and a few labeled support images per object class of interest. The training and test images do not share object classes. We extend the vanilla GAN with two loss functions, both aimed at self-supervised learning. The first is a reconstruction loss that enforces the discriminator to reconstruct the probabilistically sampled latent code which has been used for generating the "fake" image. The second is a triplet loss that enforces the discriminator to output image encodings that are closer for more similar images. Evaluation, comparisons, and detailed ablation studies are done in the context of few-shot classification. Our approach significantly outperforms the state of the art on the Mini-Imagenet and Tiered-Imagenet datasets.

Open Set Domain Recognition Via Attention-Based GCN and Semantic Matching Optimization

Xinxing He, Yuan Yuan, Zhiyu Jiang

Auto-TLDR; Attention-based GCN and Semantic Matching Optimization for Open Set Domain Recognition

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Open set domain recognition has got the attention in recent years. The task aims to specifically classify each sample in the practical unlabeled target domain, which consists of all known classes in the manually labeled source domain and target-specific unknown categories. The absence of annotated training data or auxiliary attribute information for unknown categories makes this task especially difficult. Moreover, exiting domain discrepancy in label space and data distribution further distracts the knowledge transferred from known classes to unknown classes. To address these issues, this work presents an end-to-end model based on attention-based GCN and semantic matching optimization, which first employs the attention mechanism to enable the central node to learn more discriminating representations from its neighbors in the knowledge graph. Moreover, a coarse-to-fine semantic matching optimization approach is proposed to progressively bridge the domain gap. Experimental results validate that the proposed model not only has superiority on recognizing the images of known and unknown classes, but also can adapt to various openness of the target domain.

Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?

Xiang Deng, Zhongfei Zhang

Auto-TLDR; Meta-learning Based Training of Deep Neural Networks for Few-Shot Learning

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Substantial efforts have been made on improving the generalization abilities of deep neural networks (DNNs) in order to obtain better performances without introducing more parameters. On the other hand, meta-learning approaches exhibit powerful generalization on new tasks in few-shot learning. Intuitively, few-shot learning is more challenging than the standard supervised learning as each target class only has a very few or no training samples. The natural question that arises is whether the meta-learning idea can be used for improving the generalization of DNNs on the standard supervised learning. In this paper, we propose a novel meta-learning based training procedure (MLTP) for DNNs and demonstrate that the meta-learning idea can indeed improve the generalization abilities of DNNs. MLTP simulates the meta-training process by considering a batch of training samples as a task. The key idea is that the gradient descent step for improving the current task performance should also improve a new task performance, which is ignored by the current standard procedure for training neural networks. MLTP also benefits from all the existing training techniques such as dropout, weight decay, and batch normalization. We evaluate MLTP by training a variety of small and large neural networks on three benchmark datasets, i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet. The experimental results show a consistently improved generalization performance on all the DNNs with different sizes, which verifies the promise of MLTP and demonstrates that the meta-learning idea is indeed able to improve the generalization of DNNs on the standard supervised learning.

Semantics to Space(S2S): Embedding Semantics into Spatial Space for Zero-Shot Verb-Object Query Inferencing

Sungmin Eum, Heesung Kwon

Auto-TLDR; Semantics-to-Space: Deep Zero-Shot Learning for Verb-Object Interaction with Vectors

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We present a novel deep zero-shot learning (ZSL) model for inferencing human-object-interaction with verb-object (VO) query. While the previous two-stream ZSL approaches only use the semantic/textual information to be fed into the query stream, we seek to incorporate and embed the semantics into the visual representation stream as well. Our approach is powered by Semantics-to-Space (S2S) architecture where semantics derived from the residing objects are embedded into a spatial space of the visual stream. This architecture allows the co-capturing of the semantic attributes of the human and the objects along with their location/size/silhouette information. To validate, we have constructed a new dataset, Verb-Transferability 60 (VT60). VT60 provides 60 different VO pairs with overlapping verbs tailored for testing two-stream ZSL approaches with VO query. Experimental evaluations show that our approach not only outperforms the state-of-the-art, but also shows the capability of consistently improving performance regardless of which ZSL baseline architecture is used.

Attention Based Multi-Instance Thyroid Cytopathological Diagnosis with Multi-Scale Feature Fusion

Shuhao Qiu, Yao Guo, Chuang Zhu, Wenli Zhou, Huang Chen

Auto-TLDR; A weakly supervised multi-instance learning framework based on attention mechanism with multi-scale feature fusion for thyroid cytopathological diagnosis

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In recent years, deep learning has been popular in combining with cytopathology diagnosis. Using the whole slide images (WSI) scanned by electronic scanners at clinics, researchers have developed many algorithms to classify the slide (benign or malignant). However, the key area that support the diagnosis result can be relatively small in a thyroid WSI, and only the global label can be acquired, which make the direct use of the strongly supervised learning framework infeasible. What’s more, because the clinical diagnosis of the thyroid cells requires the use of visual features in different scales, a generic feature extraction way may not achieve good performance. In this paper, we propose a weakly supervised multi-instance learning framework based on attention mechanism with multi-scale feature fusion (MSF) using convolutional neural network (CNN) for thyroid cytopathological diagnosis. We take each WSI as a bag, each bag contains multiple instances which are the different regions of the WSI, our framework is trained to learn the key area automatically and make the classification. We also propose a feature fusion structure, merge the low-level features into the final feature map and add an instance-level attention module in it, which improves the classification accuracy. Our model is trained and tested on the collected clinical data, reaches the accuracy of 93.2%, which outperforms the other existing methods. We also tested our model on a public histopathology dataset and achieves better result than the state-of-the-art deep multi-instance method.

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.

Image Representation Learning by Transformation Regression

Xifeng Guo, Jiyuan Liu, Sihang Zhou, En Zhu, Shihao Dong

Auto-TLDR; Self-supervised Image Representation Learning using Continuous Parameter Prediction

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Self-supervised learning is a thriving research direction since it can relieve the burden of human labeling for machine learning by seeking for supervision from data instead of human annotation. Although demonstrating promising performance in various applications, we observe that the existing methods usually model the auxiliary learning tasks as classification tasks with finite discrete labels, leading to insufficient supervisory signals, which in turn restricts the representation quality. In this paper, to solve the above problem and make full use of the supervision from data, we design a regression model to predict the continuous parameters of a group of transformations, i.e., image rotation, translation, and scaling. Surprisingly, this naive modification stimulates tremendous potential from data and the resulting supervisory signal has largely improved the performance of image representation learning. Extensive experiments on four image datasets, including CIFAR10, CIFAR100, STL10, and SVHN, indicate that our proposed algorithm outperforms the state-of-the-art unsupervised learning methods by a large margin in terms of classification accuracy. Crucially, we find that with our proposed training mechanism as an initialization, the performance of the existing state-of-the-art classification deep architectures can be preferably improved.

Large-Scale Historical Watermark Recognition: Dataset and a New Consistency-Based Approach

Xi Shen, Ilaria Pastrolin, Oumayma Bounou, Spyros Gidaris, Marc Smith, Olivier Poncet, Mathieu Aubry

Auto-TLDR; Historical Watermark Recognition with Fine-Grained Cross-Domain One-Shot Instance Recognition

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Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representations, both subtle differences between classes and high intra-class variation, historical watermarks are also challenging for pattern recognition. In this paper, overcoming the difficulty of data collection, we present a large public dataset with more than 6k new photographs, allowing for the first time to tackle at scale the scenarios of practical interest for scholars: one-shot instance recognition and cross-domain one-shot instance recognition amongst more than 16k fine-grained classes. We demonstrate that this new dataset is large enough to train modern deep learning approaches, and show that standard methods can be improved considerably by using mid-level deep features. More precisely, we design both a matching score and a feature fine-tuning strategy based on filtering local matches using spatial consistency. This consistency-based approach provides important performance boost compared to strong baselines. Our model achieves 55\% as top-1 accuracy on our very challenging 16,753-class one-shot cross-domain recognition task, each class described by a single drawing from the classic Briquet catalog. In addition to watermark classification, we show our approach provides promising results on fine-grained sketch-based image retrieval.

Feature-Dependent Cross-Connections in Multi-Path Neural Networks

Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe, Kumara Kahatapitiya, Subha Fernando, Ranga Rodrigo

Auto-TLDR; Multi-path Networks for Adaptive Feature Extraction

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Learning a particular task from a dataset, samples in which originate from diverse contexts, is challenging, and usually addressed by deepening or widening standard neural networks. As opposed to conventional network widening, multi-path architectures restrict the quadratic increment of complexity to a linear scale. However, existing multi-column/path networks or model ensembling methods do not consider any feature-dependant allocation of parallel resources, and therefore, tend to learn redundant features. Given a layer in a multi-path network, if we restrict each path to learn a context-specific set of features and introduce a mechanism to intelligently allocate incoming feature maps to such paths, each path can specialize in a certain context, reducing the redundancy and improving the quality of extracted features. This eventually leads to better-optimized usage of parallel resources. To do this, we propose inserting feature-dependant cross-connections between parallel sets of feature maps in successive layers. The weights of these cross-connections are learned based on the input features of the particular layer. Our multi-path networks show improved image recognition accuracy at a similar complexity compared to conventional and state-of-the-art methods for deepening, widening and adaptive feature extracting, in both small and large scale datasets.

Directed Variational Cross-encoder Network for Few-Shot Multi-image Co-segmentation

Sayan Banerjee, Divakar Bhat S, Subhasis Chaudhuri, Rajbabu Velmurugan

Auto-TLDR; Directed Variational Inference Cross Encoder for Class Agnostic Co-Segmentation of Multiple Images

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In this paper, we propose a novel framework for class agnostic co-segmentation of multiple images using comparatively smaller datasets. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning. We employ a combination of the proposed variational encoder-decoder and a novel few-shot learning approach to tackle the small sample size problem in co-segmentation. Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic. Through exhaustive experimentation using a small volume of data over multiple datasets, we have demonstrated that our approach outperforms all existing state-of-the-art techniques.

Norm Loss: An Efficient yet Effective Regularization Method for Deep Neural Networks

Theodoros Georgiou, Sebastian Schmitt, Thomas Baeck, Wei Chen, Michael Lew

Auto-TLDR; Weight Soft-Regularization with Oblique Manifold for Convolutional Neural Network Training

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Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to address these issues, researchers develop weight regularization methods and activation normalization methods. In this work we propose a weight soft-regularization method based on the Oblique manifold. The proposed method uses a loss function which pushes each weight vector to have a norm close to one, i.e. the weight matrix is smoothly steered toward the so-called Oblique manifold. We evaluate our method on the very popular CIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art architectures, namely the ResNet and wide-ResNet. Our method introduces negligible computational overhead and the results show that it is competitive to the state-of-the-art and in some cases superior to it. Additionally, the results are less sensitive to hyperparameter settings such as batch size and regularization factor.

Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data

Nakamasa Inoue, Eisuke Yamagata, Hirokatsu Kataoka

Auto-TLDR; Network Initialization Using Perlin Noise for Image Classification

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We propose a novel network initialization method using Perlin noise for training image classification networks with a limited amount of data. Our main idea is to initialize the network parameters by solving an artificial noise classification problem, where the aim is to classify Perlin noise samples into their noise categories. Specifically, the proposed method consists of two steps. First, it generates Perlin noise samples with category labels defined based on noise complexity. Second, it solves a classification problem, in which network parameters are optimized to classify the generated noise samples. This method produces a reasonable set of initial weights (filters) for image classification. To the best of our knowledge, this is the first work to initialize networks by solving an artificial optimization problem without using any real-world images. Our experiments show that the proposed method outperforms conventional initialization methods on four image classification datasets.

Heterogeneous Graph-Based Knowledge Transfer for Generalized Zero-Shot Learning

Junjie Wang, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenjie Zhang, Hongyuan Zha

Auto-TLDR; Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-Shot Learning

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Generalized zero-shot learning (GZSL) tackles the problem of learning to classify instances involving both seen classes and unseen ones. The key issue is how to effectively transfer the model learned from seen classes to unseen classes. Existing works in GZSL usually assume that some prior information about unseen classes are available. However, such an assumption is unrealistic when new unseen classes appear dynamically. To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network. Specifically, a structured heterogeneous graph is constructed with high-level representative nodes for seen classes, which are chosen through Wasserstein barycenter in order to simultaneously capture inter-class and intra-class relationship. The aggregation and embedding functions can be learned throughgraph neural network, which can be used to compute the embeddings of unseen classes by transferring the knowledge from their neighbors. Extensive experiments on public benchmark datasets show that our method achieves state-of-the-art results.

A Few-Shot Learning Approach for Historical Ciphered Manuscript Recognition

Mohamed Ali Souibgui, Alicia Fornés, Yousri Kessentini, Crina Tudor

Auto-TLDR; Handwritten Ciphers Recognition Using Few-Shot Object Detection

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Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.

Feature Fusion for Online Mutual Knowledge Distillation

Jangho Kim, Minsung Hyun, Inseop Chung, Nojun Kwak

Auto-TLDR; Feature Fusion Learning Using Fusion of Sub-Networks

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We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks and generates meaningful feature maps. Specifically, we train a number of parallel neural networks as sub-networks, then we combine the feature maps from each sub-network using a fusion module to create a more meaningful feature map. The fused feature map is passed into the fused classifier for overall classification. Unlike existing feature fusion methods, in our framework, an ensemble of sub-network classifiers transfers its knowledge to the fused classifier and then the fused classifier delivers its knowledge back to each sub-network, mutually teaching one another in an online-knowledge distillation manner. This mutually teaching system not only improves the performance of the fused classifier but also obtains performance gain in each sub-network. Moreover, our model is more beneficial than other alternative methods because different types of network can be used for each sub-network. We have performed a variety of experiments on multiple datasets such as CIFAR-10, CIFAR-100 and ImageNet and proved that our method is more effective than other alternative methods in terms of performances of both sub-networks and the fused classifier, and the aspect of generating meaningful feature maps.

Multi-Attribute Learning with Highly Imbalanced Data

Lady Viviana Beltran Beltran, Mickaël Coustaty, Nicholas Journet, Juan C. Caicedo, Antoine Doucet

Auto-TLDR; Data Imbalance in Multi-Attribute Deep Learning Models: Adaptation to face each one of the problems derived from imbalance

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Data is one of the most important keys for success when studying a simple or a complex phenomenon. With the use of deep-learning exploding and its democratization, non-computer science experts may struggle to use highly complex deep learning architectures, even when straightforward models offer them suitable performances. In this article, we study the specific and common problem of data imbalance in real databases as most of the bad performance problems are due to the data itself. We review two points: first, when the data contains different levels of imbalance. Classical imbalanced learning strategies cannot be directly applied when using multi-attribute deep learning models, i.e., multi-task and multi-label architectures. Therefore, one of our contributions is our proposed adaptations to face each one of the problems derived from imbalance. Second, we demonstrate that with little to no imbalance, straightforward deep learning models work well. However, for non-experts, these models can be seen as black boxes, where all the effort is put in pre-processing the data. To simplify the problem, we performed the classification task ignoring information that is costly to extract, such as part localization which is widely used in the state of the art of attribute classification. We make use of a widely known attribute database, CUB-200-2011 - CUB as our main use case due to its deeply imbalanced nature, along with two better structured databases: celebA and Awa2. All of them contain multi-attribute annotations. The results of highly fine-grained attribute learning over CUB demonstrate that in the presence of imbalance, by using our proposed strategies is possible to have competitive results against the state of the art, while taking advantage of multi-attribute deep learning models. We also report results for two better-structured databases over which our models over-perform the state of the art.

Parallel Network to Learn Novelty from the Known

Shuaiyuan Du, Chaoyi Hong, Zhiyu Pan, Chen Feng, Zhiguo Cao

Auto-TLDR; Trainable Parallel Network for Pseudo-Novel Detection

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Towards multi-class novelty detection, we propose an end-to-end trainable Parallel Network (PN) using no additional data but only the training set itself. Our key idea is to first divide the training set into successive subtasks of pseudo-novelty detection to simulate real scenarios. We then design a multi-branch PN to well address the fine-grained division, which yields a compressed and more discriminative classification space and forms a natural ensemble. In practice, we divide the training data into subsets consisting of known and pseudo-novel classes. Each subset forms a sub-task fed to one branch in PN. During training, both known and pseudo-novel classes are uniformly distributed over the branches for better data balance and model diversity. By distinguishing between the known and the diverse pseudo-novel, PN extracts the concept of novelty in a compressed classification space. This provides PN with generalization ability to real novel classes which are absent during training. During online inference, this ability is further strengthened with the ensemble of PN's multiple branches. Experiments on three public datasets show our method's superiority to the mainstream methods.

Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks

Sebastian Palacio, Philipp Engler, Jörn Hees, Andreas Dengel

Auto-TLDR; Self-Supervised Autogenous Learning for Deep Neural Networks

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Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e.g., minimization of the cross- entropy loss). This setup dismisses all kinds of supporting signals that can be used to reinforce the existence or absence of particular patterns. The increasing need for models that are interpretable by design makes the inclusion of said contextual signals a crucial necessity. To this end, we introduce the notion of Self-Supervised Autogenous Learning (SSAL). A SSAL objective is realized through one or more additional targets that are derived from the original supervised classification task, following architectural principles found in multi-task learning. SSAL branches impose low-level priors into the optimization process (e.g., grouping). The ability of using SSAL branches during inference, allow models to converge faster, focusing on a richer set of class-relevant features. We equip state-of-the-art DNNs with SSAL objectives and report consistent improvements for all of them on CIFAR100 and Imagenet. We show that SSAL models outperform similar state-of-the-art methods focused on contextual loss functions, auxiliary branches and hierarchical priors.

ARCADe: A Rapid Continual Anomaly Detector

Ahmed Frikha, Denis Krompass, Volker Tresp

Auto-TLDR; ARCADe: A Meta-Learning Approach for Continuous Anomaly Detection

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Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored. The present work addresses a learning scenario where a model has to incrementally learn a sequence of anomaly detection tasks, i.e. tasks from which only examples from the normal (majority) class are available for training. We define this novel learning problem of continual anomaly detection (CAD) and formulate it as a meta-learning problem. Moreover, we propose \emph{A Rapid Continual Anomaly Detector (ARCADe)}, an approach to train neural networks to be robust against the major challenges of this new learning problem, namely catastrophic forgetting and overfitting to the majority class. The results of our experiments on three datasets show that, in the CAD problem setting, ARCADe substantially outperforms baselines from the continual learning and anomaly detection literature. Finally, we provide deeper insights into the learning strategy yielded by the proposed meta-learning algorithm.

Generalized Local Attention Pooling for Deep Metric Learning

Carlos Roig Mari, David Varas, Issey Masuda, Juan Carlos Riveiro, Elisenda Bou-Balust

Auto-TLDR; Generalized Local Attention Pooling for Deep Metric Learning

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Deep metric learning has been key to recent advances in face verification and image retrieval amongst others. These systems consist on a feature extraction block (extracts feature maps from images) followed by a spatial dimensionality reduction block (generates compact image representations from the feature maps) and an embedding generation module (projects the image representation to the embedding space). While research on deep metric learning has focused on improving the losses for the embedding generation module, the dimensionality reduction block has been overlooked. In this work, we propose a novel method to generate compact image representations which uses local spatial information through an attention mechanism, named Generalized Local Attention Pooling (GLAP). This method, instead of being placed at the end layer of the backbone, is connected at an intermediate level, resulting in lower memory requirements. We assess the performance of the aforementioned method by comparing it with multiple dimensionality reduction techniques, demonstrating the importance of using attention weights to generate robust compact image representations. Moreover, we compare the performance of multiple state-of-the-art losses using the standard deep metric learning system against the same experiment with our GLAP. Experiments showcase that the proposed Generalized Local Attention Pooling mechanism outperforms other pooling methods when compared with current state-of-the-art losses for deep metric learning.

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.

Revisiting Graph Neural Networks: Graph Filtering Perspective

Hoang Nguyen-Thai, Takanori Maehara, Tsuyoshi Murata

Auto-TLDR; Two-Layers Graph Convolutional Network with Graph Filters Neural Network

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In this work, we develop quantitative results to the learnability of a two-layers Graph Convolutional Network (GCN). Instead of analyzing GCN under some classes of functions, our approach provides a quantitative gap between a two-layers GCN and a two-layers MLP model. From the graph signal processing perspective, we provide useful insights to some flaws of graph neural networks for vertex classification. We empirically demonstrate a few cases when GCN and other state-of-the-art models cannot learn even when true vertex features are extremely low-dimensional. To demonstrate our theoretical findings and propose a solution to the aforementioned adversarial cases, we build a proof of concept graph neural network model with different filters named Graph Filters Neural Network (gfNN).

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

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).

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.

Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-Identification

Toby Breckon, Aishah Alsehaim

Auto-TLDR; ResNet50-IBN for Video-based Person Re-Identification using Single Stream 2D Convolution Network

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Video-based person re-identification has received increasing attention recently, as it plays an important role within the surveillance video analysis. Video-based Re-ID is an expansion of earlier image-based re-identification methods by learning features from a video via multiple image frames for each person. Most contemporary video Re-ID methods utilise complex CNN-based network architectures using 3D convolution or multi-branch networks to extract spatial-temporal features from the video. By contrast, in this paper, we will illustrate superior performance from a simple single stream 2D convolution network leveraging the ResNet50-IBN architecture to extract frame-level features followed by temporal attention for clip level features. These clip level features can be generalised to extract video level features by averaging clip level features without any additional cost. Our model, uses best video Re-ID practice and transfer learning between datasets, outperforms existing state-of-the-art approaches on MARS, PRID2011 and iLIDSVID datasets with 89:62%, 97:75%, 97:33% rank-1 accuracy respectively and with 84:61% mAP for MARS, without reliance on complex and memory intensive 3D convolutions or multistream networks architectures as found in other contemporary work. Conversely, this work shows that global features extracted by the 2D convolution network are a sufficient representation for robust state of the art video Re-ID.

Prior Knowledge about Attributes: Learning a More Effective Potential Space for Zero-Shot Recognition

Chunlai Chai, Yukuan Lou

Auto-TLDR; Attribute Correlation Potential Space Generation for Zero-Shot Learning

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Zero-shot learning (ZSL) aims to recognize unseen classes accurately by learning seen classes and known attributes, but correlations in attributes were ignored by previous study which lead to classification results confused. To solve this problem, we build an Attribute Correlation Potential Space Generation (ACPSG) model which uses a graph convolution network and attribute correlation to generate a more discriminating potential space. Combining potential discrimination space and user-defined attribute space, we can better classify unseen classes. Our approach outperforms some existing state-of-the-art methods on several benchmark datasets, whether it is conventional ZSL or generalized ZSL.

Slimming ResNet by Slimming Shortcut

Donggyu Joo, Doyeon Kim, Junmo Kim

Auto-TLDR; SSPruning: Slimming Shortcut Pruning on ResNet Based Networks

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Conventional network pruning methods on convolutional neural networks (CNNs) reduce the number of input or output channels of convolution layers. With these approaches, the channels in the plain network can be pruned without any restrictions. However, in case of the ResNet based networks which have shortcuts (skip connections), the channel slimming of existing pruning methods is limited to the inside of each residual block. Since the number of Flops and parameters are also highly related to the number of channels in the shortcuts, more investigation on pruning channels in shortcuts is required. In this paper, we propose a novel pruning method, Slimming Shortcut Pruning (SSPruning), for pruning channels in shortcuts on ResNet based networks. First, we separate the long shortcut in individual regions that can be pruned independently without considering its long connections. Then, by applying our Importance Learning Gate (ILG) which learns the importance of channels globally regardless of channel type and location (i.e., in the shortcut or inside of the block), we can finally achieve an optimally pruned model. Through various experiments, we have confirmed that our method yields outstanding results when we prune the shortcuts and inside of the block together.

P-DIFF: Learning Classifier with Noisy Labels Based on Probability Difference Distributions

Wei Hu, Qihao Zhao, Yangyu Huang, Fan Zhang

Auto-TLDR; P-DIFF: A Simple and Effective Training Paradigm for Deep Neural Network Classifier with Noisy Labels

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Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over- fit on these noisy labels due to its high capability. In this paper, we present a very simple but effective training paradigm called P-DIFF, which can train DNN classifiers but obviously alleviate the adverse impact of noisy labels. Our proposed probability difference distribution implicitly reflects the probability of a training sample to be clean, then this probability is employed to re-weight the corresponding sample during the training process. P-DIFF can also achieve good performance even without prior- knowledge on the noise rate of training samples. Experiments on benchmark datasets also demonstrate that P-DIFF is superior to the state-of-the-art sample selection methods.

Recognizing Bengali Word Images - A Zero-Shot Learning Perspective

Sukalpa Chanda, Daniël Arjen Willem Haitink, Prashant Kumar Prasad, Jochem Baas, Umapada Pal, Lambert Schomaker

Auto-TLDR; Zero-Shot Learning for Word Recognition in Bengali Script

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Zero-Shot Learning(ZSL) techniques could classify a completely unseen class, which it has never seen before during training. Thus, making it more apt for any real-life classification problem, where it is not possible to train a system with annotated data for all possible class types. This work investigates recognition of word images written in Bengali Script in a ZSL framework. The proposed approach performs Zero-Shot word recognition by coupling deep learned features procured from VGG16 architecture along with 13 basic shapes/stroke primitives commonly observed in Bengali script characters. As per the notion of ZSL framework those 13 basic shapes are termed as “Signature Attributes”. The obtained results are promising while evaluation was carried out in a Five-Fold cross-validation setup dealing with samples from 250 word classes.

Video Representation Fusion Network For Multi-Label Movie Genre Classification

Tianyu Bi, Dmitri Jarnikov, Johan Lukkien

Auto-TLDR; A Video Representation Fusion Network for Movie Genre Classification

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In this paper, we introduce a Video Representation Fusion Network (VRFN) for movie genre classification. Different from the previous works, which use frame-level features for movie genre classification, our approach uses video classification architecture to create video-level features from a group of frames and fuse these features temporally to learn long-term spatiotemporal information for the movie genre classification task. We use a pre-trained I3D model to generate intermediate video representations and connect it with a C3D-LSTM model for feature fusion and movie genre classification. LMTD-9 dataset which contains 4007 trailers multi-labeled with 9 movie genres is used for training and evaluation of the model. The experimental results demonstrate that learning long-term temporal dependencies by fusing video representations improves the performance in movie genre classification. Our best model outperforms the state-of-the-art methods by 3.4% improvement in AUPRC (macro).

Dynamic Multi-Path Neural Network

Yingcheng Su, Yichao Wu, Ken Chen, Ding Liang, Xiaolin Hu

Auto-TLDR; Dynamic Multi-path Neural Network

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Although deeper and larger neural networks have achieved better performance, due to overwhelming burden on computation, they cannot meet the demands of deployment on resource-limited devices. An effective strategy to address this problem is to make use of dynamic inference mechanism, which changes the inference path for different samples at runtime. Existing methods only reduce the depth by skipping an entire specific layer, which may lose important information in this layer. In this paper, we propose a novel method called Dynamic Multi-path Neural Network (DMNN), which provides more topology choices in terms of both width and depth on the fly. For better modelling the inference path selection, we further introduce previous state and object category information to guide the training process. Compared to previous dynamic inference techniques, the proposed method is more flexible and easier to incorporate into most modern network architectures. Experimental results on ImageNet and CIFAR-100 demonstrate the superiority of our method on both efficiency and classification accuracy.

Learnable Higher-Order Representation for Action Recognition

Jie Shao, Xiangyang Xue

Auto-TLDR; Learningable Higher-Order Operations for Spatiotemporal Dynamics in Video Recognition

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Capturing spatiotemporal dynamics is an essential topic in video recognition. In this paper, we present learnable higher-order operations as a generic family of building blocks for capturing spatiotemporal dynamics from RGB input video space. Similar to higher-order functions, the weights of higher-order operations are themselves derived from the data with learnable parameters. Classical architectures such as residual learning and network-in-network are first-order operations where weights are directly learned from the data. Higher-order operations make it easier to capture context-sensitive patterns, such as motion. Self-attention models are also higher-order operations, but the attention weights are mostly computed from an affine operation or dot product. The learnable higher-order operations can be more generic and flexible. Experimentally, we show that on the task of video recognition, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on Something-Something (V1 and V2), Kinetics and Charades datasets.

What Nodes Vote To? Graph Classification without Readout Phase

Yuxing Tian, Zheng Liu, Weiding Liu, Zeyu Zhang, Yanwen Qu

Auto-TLDR; node voting based graph classification with convolutional operator

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In recent years, many researchers have started to construct Graph Neural Networks (GNNs) to deal with graph classification task. Those GNNs can fit into a framework named Message Passing Neural Networks (MPNNs), which consists of two phases: a Message Passing phase used for updating node embeddings and a Readout phase. In Readout phase, node embeddings are aggregated to extract graph feature used for classification. However, the above operation may obscure the affect of the node embedding of each node on graph classification. Therefore, a node voting based graph classification model is proposed in this paper, called Node Voting net (NVnet). Similar to the MPNNs, NVnet also contains the Message Passing phase. The main differences between NVnet and MPNNs are: 1, a decoder for graph reconstruction is added to NVnet to make node embeddings contain as much graph structure information as possible; 2, NVnet replaces the Readout phase with a new phase called Node Voting phase. In the Node Voting phase, an attention layer based on the gate mechanism is constructed to help each node observe the node embeddings of other nodes in the graph, and each node predicts the graph class from its own perspective. The above process is called node voting. After voting, the results of all nodes are aggregated to get the final graph classification result. In addition, considering that aggregation operation may also obscure the difference between node voting results, our solution is to add a regularization term to drive node voting results to reach group consensus. We evaluate the performance of the NVnet on 4 benchmark datasets. The experimental results show that compared with other 10 baselines, NVnet can achieve higher graph classification accuracy on datasets by using appropriate convolutional operator.