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Meta Agent Teaming Active Learning for Pose Estimation

Meta Agent Teaming Active Learning for Pose Estimation

引用

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

作者： Gong, Jia Fan, Zhipeng Ke, Qiuhong Rahmani, Hossein Liu, Jun Singapore Univ Technol & Design Singapore Singapore NYU New York NY 10003 USA Univ Melbourne Parkville Vic Australia Univ Lancaster Lancaster England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire. To reduce the human efforts on pose annotations, we propose a novel Meta Agent Teaming Active Learning (MATAL) framework to actively select and label informative images for effective learning. Our MATAL formulates the image selection procedure as a Markov Decision Process and learns an optimal sampling policy that directly maximizes the performance of the pose estimator based on the reward. Our framework consists of a novel state-action representation as well as a multi-agent team to enable batch sampling in the active learning procedure. The framework could be effectively optimized via Meta-Optimization to accelerate the adaptation to the gradually expanded labeled data during deployment. Finally, we show experimental results on both human hand and body pose estimation benchmark datasets and demonstrate that our method significantly outperforms all baselines continuously under the same amount of annotation budget. Moreover, to obtain similar pose estimation accuracy, our MATAL framework can save around 40% labeling efforts on average compared to state-of-the-art active learning frameworks.

关键词： computer vision Annotations Computational modeling Accelerated aging Pose estimation Markov processes Benchmark testing

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DynamicDet: A Unified Dynamic Architecture for Object Detection

DynamicDet: A Unified Dynamic Architecture for Object Detect...

引用

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

作者： Lin, Zhihao Wang, Yongtao Zhang, Jinhe Chu, Xiaojie Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Dynamic neural network is an emerging research topic in deep learning. With adaptive inference, dynamic models can achieve remarkable accuracy and computational efficiency. However, it is challenging to design a powerful dynamic detector, because of no suitable dynamic architecture and exiting criterion for object detection. To tackle these difficulties, we propose a dynamic framework for object detection, named DynamicDet. Firstly, we carefully design a dynamic architecture based on the nature of the object detection task. Then, we propose an adaptive router to analyze the multi-scale information and to decide the inference route automatically. We also present a novel optimization strategy with an exiting criterion based on the detection losses for our dynamic detectors. Last, we present a variable-speed inference strategy, which helps to realize a wide range of accuracy-speed trade-offs with only one dynamic detector. Extensive experiments conducted on the COCO benchmark demonstrate that the proposed DynamicDet achieves new state-of-the-art accuracy-speed trade-offs. For instance, with comparable accuracy, the inference speed of our dynamic detector Dy-YOLOv7-W6 surpasses YOLOv7-E6 by 12%, YOLOv7-D6 by 17%, and YOLOv7-E6E by 39%. The code is available at https://***/VDIGPKU/DynamicDet.

关键词： detection recognition: Categorization retrieval

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Continual Segmentation with Disentangled Objectness Learning and Class recognition

Continual Segmentation with Disentangled Objectness Learning...

引用

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

作者： Gong, Yizheng Yu, Siyue Wang, Xiaoyang Xiao, Jimin Xian Jiaotong Liverpool Univ Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England Metavisioncn Istanbul Turkiye

ISBN: (纸本)9798350353013;9798350353006

Most continual segmentation methods tackle the problem as a per-pixel classification task. However, such a paradigm is very challenging, and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones, as objectness has strong transfer ability and forgetting resistance. Based on these findings, we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning, a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes, we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMasTRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://***/jordangong/CoMasTRe.

关键词： continual learning image segmentation Transformer

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Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion recognition

Recursive Joint Cross-Modal Attention for Multimodal Fusion ...

引用

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

作者： Praveen, R. Gnana Alam, Jahangir Comp Res Inst Montreal CRIM Montreal PQ Canada

ISBN: (纸本)9798350365474

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modalities for dimensional emotion recognition. In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and intermodal relationships across the modalities. The attended features of the individual modalities are again fed as input to the fusion model in a recursive mechanism to obtain more refined feature representations. We have also explored Temporal Convolutional Networks (TCNs) to improve the temporal modeling of the feature representations of individual modalities. Extensive experiments are conducted to evaluate the performance of the proposed fusion model on the challenging Affwild2 dataset. By effectively capturing the synergic intra- and inter-modal relationships across audio, visual, and text modalities, the proposed fusion model achieves a Concordance Correlation Coefficient (CCC) of 0.585 (0.542) and 0.674 (0.619) for valence and arousal, respectively, on the validation set (test set). This shows a significant improvement over the baseline of 0.240 (0.211) and 0.200 (0.191) for valence and arousal, respectively, in the validation set (test set), achieving second place in the valence-arousal challenge of the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The code is available on GitHub: https: //***/praveena2j/RJCMA.

关键词： Concordance Correlation Coefficient Cross-Attention Dimensional Emotion recognition Joint Respresentation Multimodal Fusion Recursive Fusion

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DAVANet: Stereo Deblurring with View Aggregation 32

DAVANet: Stereo Deblurring with View Aggregation

引用

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

作者： Zhou, Shangchen Zhang, Jiawei Zuo, Wangmeng Xie, Haozhe Pan, Jinshan Ren, Jimmy SenseTime Res Harbin Peoples R China Harbin Inst Technol Harbin Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781728132938

Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles. However, they also suffer from blurry images in dynamic scenes which leads to visual discomfort and hampers further image processing. Previous works have succeeded in monocular deblurring, yet there are few studies on deblurring for stereoscopic images. By exploiting the two-view nature of stereo images, we propose a novel stereo image deblurring network with Depth Awareness and View Aggregation, named DAVANet. In our proposed network, 3D scene cues from the depth and varying information from two views are incorporated, which help to remove complex spatially-varying blur in dynamic scenes. Specifically, with our proposed fusion network, we integrate the bidirectional disparities estimation and deblurring into a unified framework. Moreover, we present a large-scale multi-scene dataset for stereo deblurring, containing 20,637 blurry-sharp stereo image pairs from 135 diverse sequences and their corresponding bidirectional disparities. The experimental results on our dataset demonstrate that DAVANet outperforms state-of-the-art methods in terms of accuracy, speed, and model size.

关键词： Deep Learning Low-level vision RGBD sensors and analytics

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Rethinking computer-aided Tuberculosis Diagnosis

Rethinking Computer-aided Tuberculosis Diagnosis

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

作者： Liu, Yun Wu, Yu-Huan Ban, Yunfeng Wang, Huifang Cheng, Ming-Ming Nankai Univ Coll Comp Sci TKLNDST Tianjin Peoples R China InferVision Beijing Peoples R China

ISBN: (纸本)9781728171685

As a serious infectious disease, tuberculosis (TB) is one of the major threats to human health worldwide, leading to millions of deaths every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. computer-aided tuberculosis diagnosis (CTD) is a promising choice for TB diagnosis due to the great successes of deep learning. However, when it comes to TB diagnosis, the lack of training data has hampered the progress of CTD. To solve this problem, we establish a large-scale TB dataset, namely Tuberculosis X-ray (TBX11K) dataset. This dataset contains 11200 X-ray images with corresponding bounding box annotations for TB areas, while the existing largest public TB dataset only has 662 X-ray images with corresponding image-level annotations. The proposed dataset enables the training of sophisticated detectors for high-quality CTD. We reform the existing object detectors to adapt them to simultaneous image classification and TB area detection. These reformed detectors are trained and evaluated on the proposed TBX11K dataset and served as the baselines for future research.

关键词： Deep learning

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CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning 32

CANet: Class-Agnostic Segmentation Networks with Iterative R...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Chi Lin, Guosheng Liu, Fayao Yao, Rui Shen, Chunhua Nanyang Technol Univ Singapore Singapore Univ Adelaide Adelaide SA Australia China Univ Min & Technol Beijing Peoples R China

ISBN: (纸本)9781728132938

Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.

关键词： Categorization Grouping and Shape recognition: Detection Retrieval Segmentation

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Stitchable Neural Networks

Stitchable Neural Networks

引用

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

作者： Pan, Zizheng Cai, Jianfei Zhuang, Bohan Monash Univ ZIP Lab Clayton Vic Australia

ISBN: (纸本)9798350301298

The public model zoo containing enormous powerful pretrained model families (e.g., ResNet/DeiT) has reached an unprecedented scope than ever, which significantly contributes to the success of deep learning. As each model family consists of pretrained models with diverse scales (e.g., DeiT-Ti/S/B), it naturally arises a fundamental question of how to efficiently assemble these readily available models in a family for dynamic accuracy-efficiency trade-offs at runtime. To this end, we present Stitchable Neural Networks (SN-Net), a novel scalable and efficient framework for model deployment. It cheaply produces numerous networks with different complexity and performance trade-offs given a family of pretrained neural networks, which we call anchors. Specifically, SN-Net splits the anchors across the blocks/layers and then stitches them together with simple stitching layers to map the activations from one anchor to another. With only a few epochs of training, SN-Net effectively interpolates between the performance of anchors with varying scales. At runtime, SN-Net can instantly adapt to dynamic resource constraints by switching the stitching positions. Extensive experiments on ImageNet classification demonstrate that SN-Net can obtain on-par or even better performance than many individually trained networks while supporting diverse deployment scenarios. For example, by stitching Swin Transformers, we challenge hundreds of models in Timm model zoo with a single network. We believe this new elastic model framework can serve as a strong baseline for further research in wider communities.

关键词： Efficient and scalable vision

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Circle Loss: A Unified Perspective of Pair Similarity Optimization

Circle Loss: A Unified Perspective of Pair Similarity Optimi...

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

作者： Sun, Yifan Cheng, Changmao Zhang, Yuhan Zhang, Chi Zheng, Liang Wang, Zhongdao Wei, Yichen MEGVII Technol Beijing Peoples R China Beihang Univ Beijing Peoples R China Australian Natl Univ Canberra ACT Australia Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781728171685

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity s(p) and minimize the between-class similarity s(n). We find a majority of loss functions, including the triplet loss and the softmax cross-entropy loss, embed sp and s p into similarity pairs and seek to reduce (s(n) - s(p)). Such an optimization manner is inflexible, because the penalty strength on every single similarity score is restricted to be equal. Our intuition is that if a similarity score deviates far from the optimum, it should be emphasized. To this end, we simply re-weight each similarity to highlight the less-optimized similarity scores. It results in a Circle loss, which is named due to its circular decision boundary. The Circle loss has a unified formula for two elemental deep feature learning paradigms, i.e., learning with class-level labels and pair-wise labels. Analytically, we show that the Circle loss offers a more flexible optimization approach towards a more definite convergence target, compared with the loss functions optimizing (s(n) - s(p)). Experimentally, we demonstrate the superiority of the Circle loss on a variety of deep feature learning tasks. On face recognition, person re-identification, as well as several fine-grained image retrieval datasets, the achieved performance is on par with the state of the art.

关键词： Face recognition

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Adapting Object Detectors via Selective Cross-Domain Alignment 32

Adapting Object Detectors via Selective Cross-Domain Alignme...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Xinge Pang, Jiangmiao Yang, Ceyuan Shi, Jianping Lin, Dahua Chinese Univ Hong Kong Hong Kong Peoples R China Zhejiang Univ Hangzhou Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781728132938

State-of-the-art object detectors are usually trained on public datasets. They often face substantial difficulties when applied to a different domain, where the imaging condition differs significantly and the corresponding annotated data are unavailable (or expensive to acquire). A natural remedy is to adapt the model by aligning the image representations on both domains. This can be achieved, for example, by adversarial learning, and has been shown to be effective in tasks like image classification. However, we found that in object detection, the improvement obtained in this way is quite limited. An important reason is that conventional domain adaptation methods strive to align images as a whole, while object detection, by nature, focuses on local regions that may contain objects of interest. Motivated by this, we propose a novel approach to domain adaption for object detection to handle the issues in "where to look" and "how to align". Our key idea is to mine the discriminative regions, namely those that are directly pertinent to object detection, and focus on aligning them across both domains. Experiments show that the proposed method performs remarkably better than existing methods with about 4% similar to 6% improvement under various domain-shift scenarios while keeping good scalability.

关键词： Categorization Grouping and Shape recognition: Detection Retrieval Scene Analysis and Understanding Segmentation

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