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DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking

<i>DyGLIP</i>: A Dynamic Graph Model with Link Prediction fo...

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

作者： Quach, Kha Gia Pha Nguyen Le, Huu Truong, Thanh-Dat Duong, Chi Nhan Minh-Triet Tran Luu, Khoa Concordia Univ Montreal PQ Canada VinAI Res Hanoi Vietnam Chalmers Univ Technol Gothenburg Sweden Univ Arkansas Fayetteville AR 72701 USA VNU HCM Univ Sci Ho Chi Minh City Vietnam

ISBN: (纸本)9781665445092

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach 1 to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets.

关键词： computer vision Pipelines Predictive models Cameras Prediction algorithms Robustness Trajectory

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Bi-GCN: Binary Graph Convolutional Network

Bi-GCN: Binary Graph Convolutional Network

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

作者： Wang, Junfu Wang, Yunhong Yang, Zhen Yang, Liang Guo, Yuanfang Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9781665445092

Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph is large. In this paper, we pioneer to propose a Binary Graph Convolutional Network (Bi-GCN), which binarizes both the network parameters and input node features. Besides, the original matrix multiplications are revised to binary operations for accelerations. According to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an average of similar to 30x for both the network parameters and input data, and accelerate the inference speed by an average of similar to 47x, on the citation networks. Meanwhile, we also design a new gradient approximation based back-propagation method to train our Bi-GCN well. Extensive experiments have demonstrated that our Bi-GCN can give a comparable performance compared to the full-precision baselines. Besides, our binarization approach can be easily applied to other GNNs, which has been verified in the experiments.

关键词： computer vision Memory management Loading Graph neural networks pattern recognition Task analysis

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The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth

The Temporal Opportunist: Self-Supervised Multi-Frame Monocu...

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

作者： Watson, Jamie Mac Aodha, Oisin Prisacariu, Victor Brostow, Gabriel Firman, Michael Niantic San Francisco CA 94111 USA Univ Edinburgh Edinburgh Midlothian Scotland Univ Oxford Oxford England UCL London England

ISBN: (纸本)9781665445092

Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at test time. The vast majority of monocular networks do not make use of this extra signal, thus ignoring valuable information that could be used to improve the predicted depth. Those that do, either use computationally expensive test-time refinement techniques or off-the-shelf recurrent networks, which only indirectly make use of the geometric information that is inherently available. We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available. Taking inspiration from multi-view stereo, we propose a deep end-to-end cost volume based approach that is trained using self-supervision only. We present a novel consistency loss that encourages the network to ignore the cost volume when it is deemed unreliable, e.g. in the case of moving objects, and an augmentation scheme to cope with static cameras. Our detailed experiments on both KITTI and Cityscapes show that we outperform all published self-supervised baselines, including those that use single or multiple frames at test time.

关键词： Training computer vision Costs Adaptive systems Computational modeling Estimation Cameras

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Use Your Head: Improving Long-Tail Video recognition

Use Your Head: Improving Long-Tail Video Recognition

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conference on computer vision and pattern recognition (cvpr)

作者： Toby Perrett Saptarshi Sinha Tilo Burghardt Majid Mirmehdi Dima Damen University of Bristol UK

This paper presents an investigation into long-tail video recognition. We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties. Most critically, they lack few-shot classes in their tails. In response, we propose new video benchmarks that better assess long-tail recognition, by sampling subsets from two datasets: SSv2 and VideoLT. We then propose a method, Long-Tail Mixed Reconstruction (LMR), which reduces overfitting to instances from few-shot classes by reconstructing them as weighted combinations of samples from head classes. LMR then employs label mixing to learn robust decision boundaries. It achieves state-of-the-art average class accuracy on EPIC-KITCHENS and the proposed SSv2-LT and VideoLT-LT. Benchmarks and code at: ***/tobyperrett/lmr

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MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

MobileDets: Searching for Object Detection Architectures for...

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

作者： Xiong, Yunyang Liu, Hanxiao Gupta, Suyog Akin, Berkin Bender, Gabriel Wang, Yongzhe Kindermans, Pieter-Jan Tan, Mingxing Singh, Vikas Chen, Bo Univ Wisconsin Madison WI 53705 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions. We discover that regular convolutions are a potent component to boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via neural architecture search. By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators. On the COCO object detection task, MobileDets outperform MobileNetV3+SSDLite by 1.7 mAP at comparable mobile CPU inference latencies. MobileDets also outperform MobileNetV2+SSDLite by 1.9 mAP on mobile CPUs, 3.7 mAP on Google EdgeTPU, 3.4 mAP on Qual-comm Hexagon DSP and 2.7 mAP on Nvidia Jetson GPU without increasing latency. Moreover MobileDets are comparable with the state-of-the-art MnasFPN on mobile CPUs even without using the feature pyramid, and achieve better mAP scores on both EdgeTPUs and DSPs with up to 2x speedup. Code and models are available in the TensorFlow Object Detection API [16]: https://***/tensor flow/models/tree/master/research/object_detection.

关键词： Convolutional codes Image edge detection Neural networks Object detection computer architecture Network architecture Search problems

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Evidential Active recognition: Intelligent and Prudent Open-World Embodied Perception

Evidential Active Recognition: Intelligent and Prudent Open-...

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conference on computer vision and pattern recognition (cvpr)

作者： Lei Fan Mingfu Liang Yunxuan Li Gang Hua Ying Wu Northwestern University Wormpex AI Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Active recognition enables robots to intelligently explore novel observations, thereby acquiring more information while circumventing undesired viewing conditions. Recent approaches favor learning policies from simulated or collected data, wherein appropriate actions are more frequently selected when the recognition is accurate. However, most recognition modules are developed under the closed-world assumption, which makes them ill-equipped to handle unexpected inputs, such as the absence of the target object in the current observation. To address this issue, we propose treating active recognition as a sequential evidence-gathering process, providing by-step uncertainty quantification and reliable prediction under the evidence combination theory. Additionally, the reward function developed in this paper effectively characterizes the merit of actions when operating in open-world environments. To evaluate the performance, we collect a dataset from an indoor simulator, encompassing various recognition challenges such as distance, occlusion levels, and visibility. Through a series of experiments on recognition and robustness analysis, we demonstrate the necessity of introducing uncertainties to active recognition and the superior performance of the proposed method.

关键词： computer vision Uncertainty Accuracy Target recognition Face recognition Reliability theory Robustness

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RankDetNet: Delving into Ranking Constraints for Object Detection

RankDetNet: Delving into Ranking Constraints for Object Dete...

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

作者： Liu, Ji Li, Dong Zheng, Rongzhang Tian, Lu Shan, Yi Xilinx Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

Modern object detection approaches cast detecting objects as optimizing two subtasks of classification and localization simultaneously. Existing methods often learn the classification task by optimizing each proposal separately and neglect the relationship among different proposals. Such detection paradigm also encounters the mismatch between classification and localization due to the inherent discrepancy of their optimization targets. In this work, we propose a ranking-based optimization algorithm for harmoniously learning to rank and localize proposals in lieu of the classification task. To this end, we comprehensively investigate three types of ranking constraints, i.e., global ranking, class-specific ranking and IoU-guided ranking losses. The global ranking loss encourages foreground samples to rank higher than background. The class-specific ranking loss ensures that positive samples rank higher than negative ones for each specific class. The IoU-guided ranking loss aims to align each pair of confidence scores with the associated pair of IoU overlap between two positive samples of a specific class. Our ranking constraints can sufficiently explore the relationships between samples from three different perspectives. They are easy-to-implement, compatible with mainstream detection frameworks and computation-free for inference. Experiments demonstrate that our RankDetNet consistently surpasses prior anchor-based and anchor-free baselines, e.g., improving RetinaNet baseline by 2.5% AP on the COCO test-dev set without bells and whistles. We also apply the proposed ranking constraints for 3D object detection and achieve improved performance, which further validates the superiority and generality of our method.

关键词： Location awareness computer vision Three-dimensional displays Costs Object detection pattern recognition Classification algorithms

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TransNAS-Bench-101: Improving transferability and Generalizability of Cross-Task Neural Architecture Search

TransNAS-Bench-101: Improving transferability and Generaliza...

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

作者： Duan, Yawen Chen, Xin Xu, Hang Chen, Zewei Liang, Xiaodan Zhang, Tong Li, Zhenguo Univ Hong Kong Hong Kong Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks. Many of them leverage transfer learning and seek to preserve, reuse, and refine network design knowledge to achieve higher efficiency in future tasks. However, the enormous computational cost and experiment complexity of cross-task NAS are imposing barriers for valuable research in this direction. Existing NAS benchmarks all focus on one type of vision task, i.e., classification. In this work, we propose TransNAS-Bench-101, a benchmark dataset containing network performance across seven tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks. This diversity provides opportunities to transfer NAS methods among tasks and allows for more complex transfer schemes to evolve. We explore two fundamentally different types of search space: cell-level search space and macro-level search space. With 7,352 backbones evaluated on seven tasks, 51,464 trained models with detailed training information are provided. With TransNAS-Bench-101, we hope to encourage the advent of exceptional NAS algorithms that raise cross-task search efficiency and generalizability to the next level. Our dataset and code will be available at Mindspore(1) and VEGA(2).

关键词： Training Knowledge engineering computer vision Design methodology Transfer learning computer architecture Benchmark testing

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Multi-shot Temporal Event Localization: a Benchmark

Multi-shot Temporal Event Localization: a Benchmark

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

作者： Liu, Xiaolong Hu, Yao Bai, Song Ding, Fei Bai, Xiang Torr, Philip H. S. Huazhong Univ Sci & Technol Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Current developments in temporal event or action localization usually target actions captured by a single camera. However, extensive events or actions in the wild may be captured as a sequence of shots by multiple cameras at different positions. In this paper, we propose a new and challenging task called multi-shot temporal event localization, and accordingly, collect a large-scale dataset called MUlti-Shot EventS (MUSES). MUSES has 31,477 event instances for a total of 716 video hours. The core nature of MUSES is the frequent shot cuts, for an average of 19 shots per instance and 176 shots per video, which induces large intra-instance variations. Our comprehensive evaluations show that the state-of-the-art method in temporal action localization only achieves an mAP of 13.1% at IoU=0.5. As a minor contribution, we present a simple baseline approach for handling the intra-instance variations, which reports an mAP of 18.9% on MUSES and 56.9% on THUMOS14 at IoU=0.5. To facilitate research in this direction, we release the dataset and the project code at https://***/muses/.

关键词： Location awareness computer vision TV Codes Annotations Benchmark testing Motion pictures

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Towards Extremely Compact RNNs for Video recognition with Fully Decomposed Hierarchical Tucker Structure

Towards Extremely Compact RNNs for Video Recognition with Fu...

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

作者： Yin, Miao Liao, Siyu Liu, Xiao-Yang Wang, Xiaodong Yuan, Bo Rutgers State Univ Newark NJ 07101 USA Amazon Seattle WA USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781665445092

Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in the resource-restricted environments is still a very challenging problem. In this paper, we propose to develop extremely compact RNN models with fully decomposed hierarchical Tucker (FDHT) structure. The HT decomposition does not only provide much higher storage cost reduction than the other tensor decomposition approaches, but also brings better accuracy performance improvement for the compact RNN models. Meanwhile, unlike the existing tensor decomposition-based methods that can only decompose the input-to-hidden layer of RNNs, our proposed fully decomposition approach enables the comprehensive compression for the entire RNN models with maintaining very high accuracy. Our experimental results on several popular video recognition datasets show that, our proposed fully decomposed hierarchical tucker-based LSTM (FDHT-LSTM) is extremely compact and highly efficient. To the best of our knowledge, FDHT-LSTM, for the first time, consistently achieves very high accuracy with only few thousand parameters (3,132 to 8,808) on different datasets. Compared with the state-of-the-art compressed RNN models, such as TT-LSTM, TR-LSTM and BT-LSTM, our FDHT-LSTM simultaneously enjoys both order-of-magnitude (3,985x to 10,711x) fewer parameters and significant accuracy improvement (0.6% to 12.7%).

关键词： computer vision Analytical models Tensors Recurrent neural networks Sequences Costs Computational modeling

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