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Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression recognition

Coarse-to-Fine Cascaded Networks with Smooth Predicting for ...

引用

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

作者： Xue, Fanglei Tan, Zichang Zhu, Yu Ma, Zhongsong Guo, Guodong Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups several similar emotions to form a rough category, and then employs a network to conduct a coarse but accurate classification. Later, an additional network for these grouped emotions is further used to obtain fine-grained predictions. For SP, it improves the recognition capability of the model by capturing both universal and unique expression features. To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment. Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We achieved 3rd place in the Expression Classification Challenge of the 3rd Competition on Affective Behavior Analysis in-the-wild. The code will be released at https://***/BR-IDL/PaddleViT.

关键词： Human computer interaction computer vision Codes Face recognition conferences Behavioral sciences

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Motion Aware Double Attention Network for Dynamic Scene Deblurring

Motion Aware Double Attention Network for Dynamic Scene Debl...

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

作者： Yang, Dan Yamac, Mehmet Huawei Technol Oy Finland Co Ltd Helsinki Finland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Motion deblurring in dynamic scenes is a challenging task when the blurring is caused by one or a combination of various reasons such as moving objects, camera movement, etc. Since event cameras can detect changes in intensity with a low latency, necessary motion information is inherently captured in event data, which could be quite useful for deblurring standard camera images. The degradation intensity does not show homogeneity across an image due to factors like object depth, speed, etc. We propose a two-branch network structure, Motion Aware Double Attention Network (MADANet), that pays special attention to areas with high blur. As part of the network, event data is first used by the high blur region segmentation module that creates a probability-like score for areas exhibiting high relative motion to the camera. Then, the event data is also injected to feature maps in the main body, where there is a second attention mechanism available for each branch. The effective usage of event data and two-level attention mechanisms makes the network very compact. During the experiment, it was shown that the proposed network could achieve state-of-the-art performance not only on the benchmark dataset from GoPro, but also on two newly collected datasets, one of which contains real event data.

关键词： Degradation Image segmentation Motion segmentation Dynamics Data visualization Cameras pattern recognition

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Cross-modal Target Retrieval for Tracking by Natural Language

Cross-modal Target Retrieval for Tracking by Natural Languag...

引用

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

作者： Li, Yihao Yu, Jun Cai, Zhongpeng Pan, Yuwen Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Tracking by natural language specification in a video is a challenging task in computer vision. Distinct from initializing the target state only by the bounding box in the first frame, language specification has a strong potential to assist visual object trackers to capture appearance variation and eliminate semantic ambiguity of the tracked object. In this paper, we carefully design a unified local-global-search framework from the perspective of cross-modal retrieval, including a local tracker, an adaptive retrieval switch module, and a target-specific retrieval module. The adaptive retrieval switch module aligns semantics from the visual signal and the lingual description of the target using three sub-modules, i.e., object-aware attention memory, part-aware cross-attention, and vision-language contrast, which achieve an automatic switch between local search and global search. When booting the global search mechanism, the target-specific retrieval module relocalizes the missing target in the image-wide range via an efficient vision-language guided proposal selector and target-text match. Numerous experimental results on three prevailing benchmarks show the effectiveness and generalization of our framework.

关键词： Visualization computer vision Target tracking Natural languages Semantics Switches Benchmark testing

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A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

A Modular Multimodal Architecture for Gaze Target Prediction...

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

作者： Gupta, Anshul Tafasca, Samy Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting where a person is looking is a complex task, requiring to understand not only the person's gaze and scene content, but also the 3D scene structure and the person's situation (are they manipulating? interacting or observing others? attentive?) to detect obstructions in the line of sight or apply attention priors that humans typically have when observing others. In this paper, we hypothesize that identifying and leveraging such priors can be better achieved through the exploitation of explicitly derived multimodal cues such as depth and pose. We thus propose a modular multimodal architecture allowing to combine these cues using an attention mechanism. The architecture can naturally be exploited in privacy-sensitive situations such as surveillance and health, where personally identifiable information cannot be released. We perform extensive experiments on the GazeFollow and VideoAttentionTarget public datasets, obtaining state-of-the-art performance and demonstrating very competitive results in the privacy setting case. (1)

关键词： Three-dimensional displays Fuses Surveillance computer architecture Predictive models Skeleton pattern recognition

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SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics

SpiderNet: Hybrid Differentiable-Evolutionary Architecture S...

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

作者： Geada, Rob McGough, Andrew Stephen Red Hat Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems. However, these algorithms require a variety of design parameters in the form of user configuration or hard-coded decisions which limit the variety of networks that can be discovered. This means that NAS algorithms do not eliminate model design tuning, they instead merely shift the burden of where that tuning needs to be applied. In this paper, we present SpiderNet, a hybrid differentiable-evolutionary and hardware-aware algorithm that rapidly and efficiently produces state-of-the-art networks. More importantly, SpiderNet is a proof-of-concept of a minimally-configured NAS algorithm;the majority of design choices seen in other algorithms are incorporated into SpiderNet's dynamically-evolving search space, minimizing the number of user choices to just two: reduction cell count and initial channel count. SpiderNet produces models highly-competitive with the state-of-the-art, and outperforms random search in accuracy, runtime, memory size, and parameter count.

关键词： Measurement Runtime Heuristic algorithms Microprocessors Neural networks computer architecture Manuals

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Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture

Multi-encoder Network for Parameter Reduction of a Kernel-ba...

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

作者： Khalifeh, Issa Blanch, Marc Gorriz Izquierdo, Ebroul Mrak, Marta British Broadcasting Corp London W12 7TQ England Queen Mary Univ London London E1 4NS England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video frame interpolation involves the synthesis of new frames from existing ones. Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this field. One popular CNN-based approach involves the application of generated kernels to the input frames to obtain an interpolated frame. Despite all the benefits interpolation methods offer, many of these networks require a lot of parameters, with more parameters meaning a heavier computational burden. Reducing the size of the model typically impacts performance negatively. This paper presents a method for parameter reduction for a popular flow-less kernel-based network (Adaptive Collaboration of Flows). Through our technique of removing the layers that require the most parameters and replacing them with smaller encoders, we reduce the number of parameters of the network and even achieve better performance compared to the original method. This is achieved by deploying rotation to force each individual encoder to learn different features from the input images. Ablations are conducted to justify design choices and an evaluation on how our method performs on full-length videos is presented.

关键词： Training Interpolation computer vision conferences Force computer architecture pattern recognition

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Few-Shot Supervised Prototype Alignment for Pedestrian Detection on Fisheye Images

Few-Shot Supervised Prototype Alignment for Pedestrian Detec...

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

作者： Wiedemer, Thaddaeus Wolf, Stefan Schumann, Arne Ma, Kaisheng Beyerer, Juergen Tsinghua Univ Beijing Peoples R China Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany

ISBN: (纸本)9781665487399

Complete and pre-trained models are readily available for download for object detection and can perform well on datasets containing everyday images. Domain adaptation is used to transfer models to more specific datasets with characteristics not present in pre-training. We propose the novel adaptation setting of pedestrian detection in fisheye images, where target samples are scarce but annotated. Our setting provides interesting new challenges for adaptation due to global perspective changes and geometric distortions not found in existing adaptation tasks. To this end, we introduce loss coupling for unsupervised adversarial adaptation and boost prototype-based adaptation with ground-truth information. We additionally propose a novel supervised adaptation head for features in the bounding box regressor. Our method leads to more stable adversarial training and outperforms supervised and unsupervised baselines. Our method requires half the amount of training samples for small datasets to achieve the same performance as supervised fine-tuning.

关键词： Training Couplings Adaptation models Schedules Surveillance Prototypes computer architecture

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Image as a Foreign Language: BEIT Pretraining for vision and vision-Language Tasks

Image as a Foreign Language: BEIT Pretraining for Vision and...

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

作者： Wang, Wenhui Bao, Hangbo Dong, Li Bjorck, Johan Peng, Zhiliang Liu, Qiang Aggarwal, Kriti Mohammed, Owais Khan Singhal, Saksham Som, Subhojit Wei, Furu Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9798350301298

A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEIT-3, which achieves excellent transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: backbone architecture, pretraining task, and model scaling up. We use Multiway Transformers for general-purpose modeling, where the modular architecture enables both deep fusion and modality-specific encoding. Based on the shared backbone, we perform masked "language" modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner. Experimental results show that BEIT-3 obtains remarkable performance on object detection (COCO), semantic segmentation (ADE20K), image classification (ImageNet), visual reasoning (NLVR2), visual question answering (VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO).

关键词： language reasoning vision

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HR-STAN: High-Resolution Spatio-Temporal Attention Network for 3D Human Motion Prediction

HR-STAN: High-Resolution Spatio-Temporal Attention Network f...

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

作者： Medjaouri, Omar Desai, Kevin Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

3D human motion prediction requires making sense of the complex spatio-temporal dynamics which underpin human motion to make highly accurate predictions. Part of this complexity is due to the trade-off between long-term (>400ms) and short-term predictions (<400ms) which require different levels of granularity to observe patterns. Several works have explored methods of improving long-term prediction performance by utilizing longer motion histories but this typically comes at the cost of very short-term (<200ms) performance. Inspired by high-resolution network architectures, we propose a novel high-resolution spatio-temporal attention network (HR-STAN) which leverages parallel feature branches and dilated convolutions to observe human motion at different scales. Furthermore, we augment this architecture with split spatial and temporal attention mechanisms to efficiently capture spatio-temporal dependencies within a given motion. We evaluate the ability of our HR-STAN architecture at incorporating long-term motion histories while producing short-term predictions and show that it improves over several state-of-the-art methods on both the AMASS and Human3.6M benchmarks.

关键词： computer vision Three-dimensional displays Costs conferences Dynamics computer architecture Predictive models

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TinyOps: ImageNet Scale Deep Learning on Microcontrollers

TinyOps: ImageNet Scale Deep Learning on Microcontrollers

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

作者： Sadiq, Sulaiman Hare, Jonathon Maji, Partha Craske, Simon Merrett, Geoff, V Univ Southampton Southampton Hants England ARM Ltd Cambridge England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep Learning on microcontroller (MCU) based IoT devices is extremely challenging due to memory constraints. Prior approaches focus on using internal memory or external memories exclusively which limit either accuracy or latency. We find that a hybrid method using internal and external MCU memories outperforms both approaches in accuracy and latency. We develop TinyOps, an inference engine which accelerates inference latency of models in slow external memory, using a partitioning and overlaying scheme via the available Direct Memory Access (DMA) peripheral to combine the advantages of external memory (size) and internal memory (speed). Experimental results show that architectures deployed with TinyOps significantly outperform models designed for internal memory with up to 6% higher accuracy and importantly, 1.3-2.2x faster inference latency to set the state-of-the-art in TinyML ImageNet classification. Our work shows that the TinyOps space is more efficient compared to the internal or external memory design spaces and should be explored further for TinyML applications.

关键词： Deep learning computer vision Microcontrollers conferences Memory management pattern recognition Internet of Things

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