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Information Elevation Network for Online Action Detection and Anticipation

Information Elevation Network for Online Action Detection an...

引用

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

作者： Min, Sunah Moon, Jinyoung Elect & Telecommun Res Inst ETRI Daejeon South Korea Univ Sci & Technol UST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Given a partially observed video segment, online action detection and anticipation aim to identify a current action and forecast future actions, respectively. To detect actions in a streaming video for monitoring applications including surveillance, robot assistants, and autonomous driving, online action detection methods have been proposed. Considering the importance of current action in online action detection, we introduce a novel information elevation unit (IEU) that lifts and accumulates the past information relevant to the current action, to compensate for forgotten essential information. Using the IEUs, we propose an information elevation network (IEN) that effectively identifies a current action and anticipates future actions through the dense prediction of past and current action classes within the video segment. For its practical use in online monitoring applications, our IEN takes visual features extracted from a fast action recognition using only RGB frames because extracting optical flows requires heavy computation overhead. On THUMOS-14 and TVSeries, our IEN outperforms state-of-the-art methods using only RGB frames. Furthermore, on the THUMOS-14 dataset, our IEN outperforms the state-of-the-art methods.

关键词： Visualization computer vision Surveillance conferences Color Streaming media Logic gates

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Natural Language-Based Vehicle Retrieval with Explicit Cross-Modal Representation Learning

Natural Language-Based Vehicle Retrieval with Explicit Cross...

引用

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

作者： Xu, Bocheng Xiong, Yihua Zhang, Rui Feng, Yanyi Wu, Haifeng Terminus Technol Dept AI R&D Beijing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

On the account of the explosive growth in the large-scale transportation videos, vehicle retrieval plays an important role in the public transportation security and the intelligent transport system recently. Most vehicle retrieval algorithms are vision-based and consist of vehicle re-identification and vehicle tracking. However, the performance of vision-based vehicle retrieval algorithms is constrained as the limited information provided by traffic video streams. In this paper, we propose a contrastive cross-modal vehicle retrieval solution, maximizing the value of the complementation between natural language representation and vision representation. The framework of the proposed solution includes: (1) Preprocess a source video in four ways for generating local motional semantics and global motional semantics;(2) Correspondingly, preprocess relevant description sentences in two ways, including Textual Local Instance Semantics Extraction (TLISE) and Textual Local Motional Semantics Extraction (TLMSE);(3) Use a two-stream architecture model with four visual encoders and four text encoders to extract visual features and textual embeddings;(4) Fuse visual features and textual embeddings respectively by concatenating them along the feature channel in the order of importance, and use them for retrieval. By using the proposed solution, we achieved MRR score of 33.20%, ranking the 7th place in the AI City Challenge 2022 Track 2.

关键词： Representation learning Visualization Semantics Urban areas computer architecture Feature extraction Robustness

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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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Visual Goal-Directed Meta-Imitation Learning

Visual Goal-Directed Meta-Imitation Learning

引用

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

作者： Rivera, Corban G. Handelman, David A. Ratto, Christopher R. Patrone, David Paulhamus, Bart L. Johns Hopkins Univ Intelligent Syst Ctr Appl Phys Lab Laurel MD 20723 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The goal of meta-learning is to generalize to new tasks and goals as quickly as possible. Ideally, we would like approaches that generalize to new goals and tasks on the first attempt. Requiring a policy to perform on a new task on the first attempt without even a single example trajectory is a zero-shot problem formulation. When tasks are identified by goal images, the tasks can be considered visually goal-directed. In this work, we explore the problem of visual goal-directed zero-shot meta-imitation learning. Inspired by several popular approaches to Meta-RL, we composed several core ideas related to task-embedding and planning by gradient descent to attempt to explore this problem. To evaluate these approaches, we adapted the Meta-world benchmark tasks to create 24 distinct visual goal-directed manipulation tasks. We found that 7 out of 24 tasks could be successfully completed on the first attempt by at least one of the approaches we tested. We demonstrated that goal-directed zero-shot approaches can translate to a physical robot with a demonstration based on Jenga block manipulation tasks using a Kinova Jaco robotic arm.

关键词： Visualization computer vision conferences Benchmark testing Manipulators Trajectory Planning

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Towards a Deeper Understanding of Skeleton-based Gait recognition

Towards a Deeper Understanding of Skeleton-based Gait Recogn...

引用

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

作者： Teepe, Torben Gilg, Johannes Herzog, Fabian Hoermann, Stefan Rigoll, Gerhard Tech Univ Munich Munich Germany

ISBN: (纸本)9781665487399

Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns. In recent years, most gait recognition methods used the person's silhouette to extract the gait features. However, silhouette images can lose fine-grained spatial information, suffer from (self) occlusion, and be challenging to obtain in real-world scenarios. Furthermore, these silhouettes also contain other visual clues that are not actual gait features and can be used for identification, but also to fool the system. Model-based methods do not suffer from these problems and are able to represent the temporal motion of body joints, which are actual gait features. The advances in human pose estimation started a new era for model-based gait recognition with skeleton-based gait recognition. In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks to an efficient architecture for gait recognition. Extensive experiments on the two popular gait datasets, CASIA-B and OUMVLP-Pose, show a massive improvement (3x) of the state-of-the-art (SotA) on the largest gait dataset OUMVLP-Pose and strong temporal modeling capabilities. Finally, we visualize our method to understand skeleton-based gait recognition better and to show that we model real gait features.

关键词： Training Visualization Pose estimation computer architecture Performance gain Feature extraction pattern recognition

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VISTA: vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout

VISTA: Vision Transformer enhanced by U-Net and Image Colorf...

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

作者： Shihab, Md Istiak Hossain Tasnim, Nazia Zunair, Hasib Rupty, Labiba Kanij Mohammed, Nabeel Shahjalal Univ Sci & Technol Sylhet Bangladesh Giga Tech Ltd Dhaka Bangladesh Concordia Univ Montreal PQ Canada North South Univ Dhaka Bangladesh

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout. The task is challenging due to the real-world scenario of occlusions where product items overlap, fast movement in conveyor belt, large similarity in overall appearance of the items being scanned, novel products, the negative impact of misidentifying items. Further there is a domain bias between training and test sets, specifically the provided training dataset consists of synthetic images and the test set videos consist of foreign objects such as hands and tray. To address these aforementioned issues, we propose to segment and classify individual frames from a video sequence. The segmentation method consists of a unified single product item- and hand-segmentation followed by entropy masking to address the domain bias problem. The multi-class classification method is based on vision Transformers (ViT). To identify the frames with target objects, we utilize several image processing methods and propose a custom metric to discard frames not having any product items. Combining all these mechanisms, our best system achieves 3rd place in the AI City Challenge 2022 Track 4 with F1 score of 0.4545.

关键词： Measurement Training Image segmentation Urban areas Video sequences Transformers Entropy

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An Empirical study of Data-Free Quantization's Tuning Robustness

An Empirical study of Data-Free Quantization's Tuning Robust...

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

作者： Chen, Hong Wen, Yuxuan Ding, Yifu Yang, Zhen Guo, Yufei Qin, Haotong Beihang Univ Beijing Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai Peoples R China China Aerosp Sci & Ind Corp Acad 2 Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep convolutional neural networks are now performing increasingly superior in various fields, while the network parameters are getting massive as the advanced neural networks tend to be deeper. Among various model compression methods, quantization is one of the most potent approaches to compress neural networks by compacting model weights and activations to lower bit-width. The data-free quantization method is also proposed, which is specialized for some privacy and security scenarios and enables quantization without access to real data. In this work, we find that the tuning robustness of existing data-free quantization is flawed, progressing an empirical study and determining some hyperparameter settings that can converge the model stably in the data-free quantization process. Our study aims to evaluate the overall tuning robustness of the current data-free quantization system, which is existing methods are significantly affected by parameter fluctuations in tuning. We also expect data-free quantification methods with tuning robustness to appear in the future.

关键词： Data privacy Quantization (signal) Fluctuations conferences Neural networks Robustness pattern recognition

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Update Compression for Deep Neural Networks on the Edge

Update Compression for Deep Neural Networks on the Edge

引用

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

作者： Chen, Bo Bakhshi, Ali Batista, Gustavo Ng, Brian Chin, Tat-Jun Univ Adelaide Adelaide SA Australia Univ New South Wales Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

An increasing number of artificial intelligence (AI) applications involve the execution of deep neural networks (DNNs) on edge devices. Many practical reasons motivate the need to update the DNN model on the edge device post-deployment, such as refining the model, concept drift, or outright change in the learning task. In this paper, we consider the scenario where retraining can be done on the server side based on a copy of the DNN model, with only the necessary data transmitted to the edge to update the deployed model. However, due to bandwidth constraints, we want to minimise the transmission required to achieve the update. We develop a simple approach based on matrix factorisation to compress the model update-this differs from compressing the model itself. The key idea is to preserve existing knowledge in the current model and optimise only small additional parameters for the update which can be used to reconstitute the model on the edge. We compared our method to similar techniques used in federated learning;our method usually requires less than half of the update size of existing methods to achieve the same accuracy.

关键词： Deep learning Neural networks Refining Redundancy Bandwidth Data models pattern recognition

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ResNeSt: Split-Attention Networks

ResNeSt: Split-Attention Networks

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

作者： Zhang, Hang Wu, Chongruo Zhang, Zhongyue Zhu, Yi Lin, Haibin Zhang, Zhi Sun, Yue He, Tong Mueller, Jonas Manmatha, R. Li, Mu Smola, Alexander Meta Menlo Pk CA 94025 USA Univ Calif Davis Davis CA USA Snap Santa Monica CA USA Amazon Seattle WA USA ByteDance Beijing Peoples R China SenseTime Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The ability to learn richer network representations generally boosts the performance of deep learning models. To improve representation-learning in convolutional neural networks, we present a multi-branch architecture, which applies channel-wise attention across different network branches to leverage the complementary strengths of both feature-map attention and multi-path representation. Our proposed Split-Attention module provides a simple and modular computation block that can serve as a drop-in replacement for the popular residual block, while producing more diverse representations via cross-feature interactions. Adding a Split-Attention module into the architecture design space of RegNet-Y and FBNetV2 directly improves the performance of the resulting network. Replacing residual blocks with our Split-Attention module, we further design a new variant of the ResNet model, named ResNeSt, which outperforms EfficientNet in terms of the accuracy/latency trade-off.

关键词： Deep learning computer vision Computational modeling conferences computer architecture pattern recognition Convolutional neural networks

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Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image recognition

Incremental Meta-Learning via Episodic Replay Distillation f...

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

作者： Wang, Kai Liu, Xialei Bagdanov, Andy Herranz, Luis Jui, Shangling van de Weijer, Joost Comp Vis Ctr Barcelona Spain Nankai Univ Tianjin Peoples R China Univ Florence MICC Florence Italy Huawei Kirin Solut Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more generalizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively.

关键词： Training computer vision Image recognition Upper bound conferences pattern recognition Task analysis

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