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Appearance-based Gaze Estimation using Attention and Difference Mechanism

Appearance-based Gaze Estimation using Attention and Differe...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Murthy, L. R. D. Biswas, Pradipta Indian Inst Sci CPDM I3D Lab Bangalore Karnataka India

ISBN: (纸本)9781665448994

Appearance-based gaze estimation problem received wide attention over the past few years. Even though model-based approaches existed earlier, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. In this paper, we proposed two novel techniques to improve gaze estimation accuracy. Our first approach, I2D-Net uses a difference layer to eliminate any common features from left and right eyes of a participant that are not pertinent to gaze estimation task. Our second approach, AGE-Net adapted the idea of attention-mechanism and assigns weights to the features extracted from eye images. I2D-Net performed on par with the existing state-of-the-art approaches while AGE-Net reported state-of-the-art accuracy of 4.09 degrees and 7.44 degrees error on MPIIGaze and RT-Gene datasets respectively. We performed ablation studies to understand the effectiveness of the proposed approaches followed by analysis of gaze error distribution with respect to various factors of MPIIGaze dataset.

关键词： Deep learning computer vision Adaptation models conferences Estimation Lighting Feature extraction

来源：评论

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Micro-expression recognition Based on Facial Graph Representation Learning and Facial Action Unit Fusion

Micro-expression Recognition Based on Facial Graph Represent...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Lei, Ling Chen, Tong Li, Shigang Li, Jianfeng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan

ISBN: (纸本)9781665448994

Micro-expressions recognition is a challenge because it involves subtle variations in facial organs. In this paper, first, we propose a novel pipeline to learn a facial graph (nodes and edges) representation to capture these local subtle variations. We express the micro-expressions with multi-patches based on facial landmarks and then stack these patches into channels while using a depthwise convolution (DConv) to learn the features inside the patches, namely, node learning. Then, the encoder of the transformer (ETran) is utilized to learn the relationships between the nodes, namely, edge learning. Based on node and edge learning, a learned facial graph representation is obtained. Second, because the occurrence of an expression is closely bound to action units, we design an AU-GCN to learn the action unit's matrix by embedding and GCN. Finally, we propose a fusion model to introduce the action unit's matrix into the learned facial graph representation. The experiments are comparing with SOTA on various evaluation criteria, including common classifications on CASME II and SAMM datasets, and also conducted following Micro-expression Grand Challenge 2019 protocol.

关键词： computer vision Protocols Convolution Face recognition conferences Pipelines Feature extraction

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DANICE: Domain adaptation without forgetting in neural image compression

DANICE: Domain adaptation without forgetting in neural image...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Katakol, Sudeep Herranz, Luis Yang, Fei Mrak, Marta Univ Michigan Ann Arbor MI 48109 USA UAB Comp Vis Ctr Barcelona Spain BBC R&D London England

ISBN: (纸本)9781665448994

Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC.

关键词： Video coding computer vision Image coding Codecs conferences Transfer learning Interference

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A Watermarking-Based Framework for Protecting Deep Image Classifiers Against Adversarial Attacks

A Watermarking-Based Framework for Protecting Deep Image Cla...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sun, Chen Yang, En-Hui Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781665448994

Although deep learning-based models have achieved tremendous success in image-related tasks, they are known to be vulnerable to adversarial examples-inputs with imperceptible, but subtly crafted perturbation which fool the models to produce incorrect outputs. To distinguish adversarial examples from benign images, in this paper, we propose a novel watermarking-based framework for protecting deep image classifiers against adversarial attacks. The proposed framework consists of a watermark encoder, a possible adversary, and a detector followed by a deep image classifier to be protected. Specific methods of watermarking and detection are also presented. It is shown by experiment on a subset of ImageNet validation dataset that the proposed framework along with the presented methods of watermarking and detection is effective against a wide range of advanced attacks (static and adaptive), achieving a near zero (effective) false negative rate for FGSM and PGD attacks (static and adaptive) with the guaranteed zero false positive rate. In addition, for all tested deep image classifiers (ResNet50V2, MobileNetV2, InceptionV3), the impact of watermarking on classification accuracy is insignificant with, on average, 0.63% and 0.49% degradation in top 1 and top 5 accuracy, respectively.

关键词： Degradation computer vision Perturbation methods conferences Watermarking Detectors pattern recognition

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Learned Video Compression with Intra-Guided Enhancement and Implicit Motion Information

Learned Video Compression with Intra-Guided Enhancement and ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zou, Nannan Zhang, Honglei Cricri, Francesco Tavakoli, Hamed R. Lainema, Jani Aksu, Emre Hannuksela, Miska Rahtu, Esa Nokia Technol Tampere Finland Tampere Univ Tampere Finland

ISBN: (纸本)9781665448994

Although learned approaches to video compression have been proposed with promising results, hand-engineered video codecs are still unbeaten. On the other hand, learned image compression has already surpassed traditional image codecs. In this paper, we propose a learned video compression system that mimics part of the pipeline of traditional codecs while leveraging learned image compression. It comprises two main modules: a learned intra-frame compression module, and a learned inter-frame compression module that is conditioned on intra-coded frames. These modules use separate learned probability models for entropy coding. The intra-frame codec uses a variant of nonlocal attention layers. Regarding the inter-frame codec, we propose an implicit motion information mechanism, and an enhancement of the inter-frame predictions by leveraging the high quality information of intra-coded frames. On the learned probability model side, we propose to use the reference frames as additional conditioning information. We used this system as our submitted entry for the 2021 Challenge on Learned Image Compression (CLIC). In our experiments, we show the effectiveness of our system and its components via a set of ablation studies.

关键词： computer vision Image coding conferences Computational modeling Pipelines MIMICs Video compression

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A Cortically-inspired Architecture for Event-based Visual Motion Processing: From Design Principles to Real-world Applications

A Cortically-inspired Architecture for Event-based Visual Mo...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Peveri, Francesca Testa, Simone Sabatini, Silvio P. Univ Genoa Dept Informat Bioengn Robot & Syst Engn Via Opera Pia 11a Genoa Italy

ISBN: (纸本)9781665448994

We developed and tested the architecture of a bio-inspired Spiking Neural Network for motion estimation. The computation performed by the retina is emulated by the neuromorphic event-based image sensor DAVIS346 which constitutes the input of our network. We obtained neurons highly tuned to spatial frequency and orientation of the stimulus through a combination of feed-forward excitatory connections modeled as an elongated Gaussian kernel and recurrent inhibitory connections from two clusters of neurons within the same cortical layers. Sums over adjacent nodes weighted by time-variable synapses are used to attain Gabor-like spatio-temporal V1 receptive fields with selectivity to the stimulus' motion. In order to gain the invariance to the stimulus phase, the two polarities of the events provided by the neuromorphic sensor were exploited, which allowed us to build two pairs of quadrature filters from which we obtain Motion Energy detectors as described in [2]. Finally, a decoding stage allows us to compute optic flow from the Motion Detector layers. We tested the approach proposed with both synthetic and natural stimuli.

关键词： Visualization Neuromorphics Neurons Neural networks computer architecture Detectors Spatial filters

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Noise Conditional Flow Model for Learning the Super-Resolution Space

Noise Conditional Flow Model for Learning the Super-Resoluti...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kim, Younggeun Son, Donghee Seoul Natl Univ Seoul South Korea Lomin Inc Seoul South Korea Deepest Seoul South Korea

ISBN: (纸本)9781665448994

Fundamentally, super-resolution is ill-posed problem because a low-resolution image can be obtained from many high-resolution images. Recent studies for super-resolution cannot create diverse super-resolution images. Although SRFlow tried to account for ill-posed nature of the super-resolution by predicting multiple high-resolution images given a low-resolution image, there is room to improve the diversity and visual quality. In this paper, we propose Noise Conditional flow model for Super-Resolution, NCSR, which increases the visual quality and diversity of images through noise conditional layer. To learn more diverse data distribution, we add noise to training data. However, low-qualit . , images are resulted from adding noise. We propose the noise conditional layer to overcome this phenomenon. The noise conditional layer makes our model generate more diverse images with higher visual quality than other works. Furthermore, we show that this layer can overcome data distribution mismatch, a problem that arises in normalizing flow models. With these benefits, NCSR outperforms baseline in diversity and visual quality and achieves better visual quality than traditional GAN-based models. We also get outperformed scores at NTIRE 2021 challenge [21].

关键词： Training Degradation Visualization computer vision Computational modeling conferences Superresolution

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Detecting and Matching Related Objects with One Proposal Multiple Predictions

Detecting and Matching Related Objects with One Proposal Mul...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yang Hafemann, Luiz G. Jamieson, Michael Javan, Mehrsan Sportlogiq Montreal PQ Canada

ISBN: (纸本)9781665448994

Tracking players in sports videos is commonly done in a tracking-by-detection framework, first detecting players in each frame, and then performing association over time. While for some sports tracking players is sufficient for game analysis, sports like hockey, tennis and polo may require additional detections, that include the object the player is holding (e.g. racket, stick). The baseline solution for this problem involves detecting these objects as separate classes, and matching them to player detections based on the intersection over union (IoU). This approach, however, leads to poor matching performance in crowded situations, as it does not model the relationship between players and objects. In this paper, we propose a simple yet efficient way to detect and match players and related objects at once without extra cost, by considering an implicit association for prediction of multiple objects through the same proposal box. We evaluate the method on a dataset of broadcast ice hockey videos, and also a new public dataset we introduce called COCO +Torso. On the ice hockey dataset, the proposed method boosts matching performance from 57.1% to 81.4%, while also improving the meanAP of player+stick detections from 68.4% to 88.3%. On the COCO +Torso dataset, we see matching improving from 47.9% to 65.2%. The COCO +Torso dataset, code and pre-trained models will be released at https: //***/foreverYoungGitHub/detectand-match-related-objects.

关键词： computer vision conferences Games Detectors Ice Proposals pattern matching

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Training Deep Generative Models in Highly Incomplete Data Scenarios with Prior Regularization

Training Deep Generative Models in Highly Incomplete Data Sc...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bernal, Edgar A. Univ Rochester Rochester Data Sci Consortium Rochester NY 14627 USA

ISBN: (纸本)9781665448994

Deep generative frameworks including GANs and normalizing flow models have proven successful at filling in missing values in partially observed data samples by effectively learning -either explicitly or implicitly- complex, high-dimensional statistical distributions. In tasks where the data available for learning is only partially observed, however, their performance decays monotonically as a function of the data missingness rate. In high missing data rate regimes (e.g., 60% and above), it has been observed that state-of-the-art models tend to break down and produce unrealistic and/or semantically inaccurate data. We propose a novel framework to facilitate the learning of data distributions in high paucity scenarios that is inspired by traditional formulations of solutions to ill-posed problems. The proposed framework naturally stems from posing the process of learning from incomplete data as a joint optimization task of the parameters of the model being learned and the missing data values. The method involves enforcing a prior regularization term that seamlessly integrates with objectives used to train explicit and tractable deep generative frameworks such as deep normalizing flow models. We demonstrate via extensive experimental validation that the proposed framework outperforms competing techniques, particularly as the rate of data paucity approaches unity.

关键词： Training computer vision Computational modeling conferences Statistical distributions Data models Filling

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Learning A Cascaded Non-Local Residual Network for Super-resolving Blurry Images

Learning A Cascaded Non-Local Residual Network for Super-res...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bai, Haoran Cheng, Songsheng Tang, Jinhui Pan, Jinshan Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665448994

Deblurring low-resolution images is quite challenging as blur exists in the images and the resolution of the images is low. Existing deblurring methods usually require high-resolution input while the super-resolution methods usually assume that the blur is known or small. Simply applying the deblurring and super-resolution does not solve this problem well. In this paper, we develop an effective cascaded non-local residual network which cascades the deblurring module and super-resolution module to estimate latent high-resolution images from blurry low-resolution ones. The network first uses the deblurring module to generate intermediate clear features and then develops a non-local residual network (NLRN) as the super-resolution module to generate clear high-resolution images from the intermediate clear features. To better constrain the network and reduce the training difficulty, we develop an effective constraint based on image gradients for edge preservation and adopt the progressive upsampling mechanism. We train the proposed network in an end-to-end manner. Both quantitative and qualitative results on the benchmarks demonstrate the effectiveness of the proposed method. Moreover, the proposed method achieves top-3 performance on the low-resolution track of the NTIRE 2021 Image Deblurring Challenge.

关键词： Training computer vision Image edge detection conferences Superresolution Benchmark testing Image restoration

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