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Open Set Action recognition via Multi-Label Evidential Learning

Open Set Action Recognition via Multi-Label Evidential Learn...

引用

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

作者： Zhao, Chen Du, Dawei Hoogs, Anthony Funk, Christopher Kitware Clifton Pk NY 12065 USA

ISBN: (纸本)9798350301298

Existing methods for open set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world. We propose a new method for open set action recognition and novelty detection via MUlti-Label Evidential learning (MULE), that goes beyond previous novel action detection methods by addressing the more general problems of single or multiple actors in the same scene, with simultaneous action(s) by any actor. Our Beta Evidential Neural Network estimates multi-action uncertainty with Beta densities based on actor-context-object relation representations. An evidence debiasing constraint is added to the objective function for optimization to reduce the static bias of video representations, which can incorrectly correlate predictions and static cues. We develop a primal-dual average scheme update-based learning algorithm to optimize the proposed problem and provide corresponding theoretical analysis. Besides, uncertainty and belief-based novelty estimation mechanisms are formulated to detect novel actions. Extensive experiments on two real-world video datasets show that our proposed approach achieves promising performance in single/multi-actor, single/multi-action settings. Our code and models are released at https://***/charliezhaoyinpeng/mule.

关键词： Video: Action and event understanding

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Continuous Sign Language recognition with Correlation Network

Continuous Sign Language Recognition with Correlation Networ...

引用

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

作者： Hu, Lianyu Gao, Liqing Liu, Zekang Feng, Wei Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

ISBN: (纸本)9798350301298

Human body trajectories are a salient cue to identify actions in the video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition (CSLR) usually process frames independently, thus failing to capture cross-frame trajectories to effectively identify a sign. To handle this limitation, we propose correlation network (CorrNet) to explicitly capture and leverage body trajectories across frames to identify signs. In specific, a correlation module is first proposed to dynamically compute correlation maps between the current frame and adjacent frames to identify trajectories of all spatial patches. An identification module is then presented to dynamically emphasize the body trajectories within these correlation maps. As a result, the generated features are able to gain an overview of local temporal movements to identify a sign. Thanks to its special attention on body trajectories, CorrNet achieves new state-of-the-art accuracy on four large-scale datasets, i.e., PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. A comprehensive comparison with previous spatial-temporal reasoning methods verifies the effectiveness of CorrNet. Visualizations demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.

关键词： body gesture Humans: Face movement pose

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ViPLO: vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection

ViPLO: Vision Transformer based Pose-Conditioned Self-Loop G...

引用

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

作者： Park, Jeeseung Park, Jin-Woo Lee, Jong-Seok MAy I Inc Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350301298

Human-Object Interaction (HOI) detection, which localizes and infers relationships between human and objects, plays an important role in scene understanding. Although two-stage HOI detectors have advantages of high efficiency in training and inference, they suffer from lower performance than one-stage methods due to the old backbone networks and the lack of considerations for the HOI perception process of humans in the interaction classifiers. In this paper, we propose vision Transformer based Pose-Conditioned Self-Loop Graph (ViPLO) to resolve these problems. First, we propose a novel feature extraction method suitable for the vision Transformer backbone, called masking with overlapped area (MOA) module. The MOA module utilizes the overlapped area between each patch and the given region in the attention function, which addresses the quantization problem when using the vision Transformer backbone. In addition, we design a graph with a pose-conditioned self-loop structure, which updates the human node encoding with local features of human joints. This allows the classifier to focus on specific human joints to effectively identify the type of interaction, which is motivated by the human perception process for HOI. As a result, ViPLO achieves the state-of-the-art results on two public benchmarks, especially obtaining a +2.07 mAP performance gain on the HICO-DET dataset.

关键词： detection recognition: Categorization retrieval

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BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View recognition via Perspective Supervision

BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-...

引用

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

作者： Yang, Chenyu Chen, Yuntao Tian, Hao Tao, Chenxin Zhu, Xizhou Zhang, Zhaoxiang Huang, Gao Li, Hongyang Qiao, Yu Lu, Lewei Zhou, Jie Dai, Jifeng Tsinghua Univ Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China SenseTime Res Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV detectors are often tied to certain depth pre-trained backbones like VoVNet, hindering the synergy between booming image backbones and BEV detectors. To address this limitation, we prioritize easing the optimization of BEV detectors by introducing perspective view supervision. To this end, we propose a two-stage BEV detector, where proposals from the perspective head are fed into the bird's-eye-view head for final predictions. To evaluate the effectiveness of our model, we conduct extensive ablation studies focusing on the form of supervision and the generality of the proposed detector. The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset. The code shall be released soon.

关键词： detection recognition: Categorization retrieval

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Spatial-temporal Concept based Explanation of 3D ConvNets

Spatial-temporal Concept based Explanation of 3D ConvNets

引用

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

作者： Ji, Ying Wang, Yu Kato, Jien Nagoya Univ Nagoya Aichi Japan Hitotsubashi Univ Kunitachi Tokyo Japan Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9798350301298

Convolutional neural networks (CNNs) have shown remarkable performance on various tasks. Despite its widespread adoption, the decision procedure of the network still lacks transparency and interpretability, making it difficult to enhance the performance further. Hence, there has been considerable interest in providing explanation and interpretability for CNNs over the last few years. Explainable artificial intelligence (XAI) investigates the relationship between input images or videos and output predictions. Recent studies have achieved outstanding success in explaining 2D image classification ConvNets. On the other hand, due to the high computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. And none of them are able to produce a high-level explanation. In this paper, we propose a STCE (Spatial-temporal Concept-based Explanation) framework for interpreting 3D ConvNets. In our approach: (1) videos are represented with high-level supervoxels, similar supervoxels are clustered as a concept, which is straightforward for human to understand;and (2) the interpreting framework calculates a score for each concept, which reflects its significance in the ConvNet decision procedure. Experiments on diverse 3D ConvNets demonstrate that our method can identify global concepts with different importance levels, allowing us to investigate the impact of the concepts on a target task, such as action recognition, in-depth. The source codes are publicly available at https://***/yingji425/STCE.

关键词： Explainable computer vision

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Content-aware Input Scaling and Deep Learning Computation Offloading for Low-Latency Embedded vision

Content-aware Input Scaling and Deep Learning Computation Of...

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

作者： Prabhune, Omkar Chen, Tianen Kim, Younghyun Purdue Univ W Lafayette IN 47907 USA Univ Wisconsin Madison WI 53706 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9798350365474

Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL applications continue to evolve, they place increasing demands on computational resources that embedded vision systems are unable to provision. One promising solution to overcome these limitations is computation offloading. However, for performance improvements to be realized, it is essential to carefully partition tasks, taking into account both the quality of the data and the communication overhead. In this paper, we introduce a novel framework for content-aware offloading of DL computations, aimed at maximizing quality-of-service while adhering to latency constraints. Our proposed framework involves the embedded vision system/edge device intelligently compressing data in a contentaware manner using a lightweight model and transmitting it to a more powerful server. The framework consists of two key components: offline training for efficient content-aware data scaling and online control that adapts to the network variations in real-time. To illustrate the effectiveness of our approach, we apply it to multiple downstream tasks such as face identification, person keypoint detection, and instance segmentation, showcasing a significant enhancement in the overall quality of results for various applications.

关键词： AI on edge computation offloading content-aware compression content-aware offloading deep learning edge computing edge ML efficient deep learning efficient ML embedded vision face identification low-latency deep learning low-power ML ml system tinyML

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Edges to Shapes to Concepts: Adversarial Augmentation for Robust vision

Edges to Shapes to Concepts: Adversarial Augmentation for Ro...

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

作者： Tripathi, Aditay Singh, Rishubh Chakraborty, Anirban Shenoy, Pradeep Indian Inst Sci CDS Bangalore Karnataka India Google Res India Bangalore Karnataka India

ISBN: (纸本)9798350301298

Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViTB) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy.

关键词： Deep learning architectures and techniques

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MAP: Multimodal Uncertainty-Aware vision-Language Pre-training Model

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-traini...

引用

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

作者： Ji, Yatai Wang, Junjie Gong, Yuan Zhang, Lin Zhu, Yanru Wang, Hongfa Zhang, Jiaxing Sakai, Tetsuya Yang, Yujiu Tsinghua Univ Beijing Peoples R China Waseda Univ Tokyo Japan IDEA Gandhinagar India Tencent TEG Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pretraining frameworks and propose suitable pre-training tasks: Distribution-based vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.

关键词： Multi-modal learning

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GamutMLP: A Lightweight MLP for Color Loss Recovery

GamutMLP: A Lightweight MLP for Color Loss Recovery

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

作者： Le, Hoang M. Price, Brian Cohen, Scott Brown, Michael S. York Univ Toronto ON Canada Adobe Res San Jose CA USA

ISBN: (纸本)9798350301298

Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image-the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing.

关键词： Low-level vision

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DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D vision

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-bas...

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

作者： Ling, Lu Sheng, Yichen Tu, Zhi Zhao, Wentian Xin, Cheng Wan, Kun Yu, Lantao Guo, Qianyu Yu, Zixun Lu, Yawen Li, Xuanmao Sun, Xingpeng Ashok, Rohan Mukherjee, Aniruddha Kang, Hao Kong, Xiangrui Hua, Gang Zhang, Tianyi Benes, Bedrich Bera, Aniket Purdue Univ W Lafayette IN 47907 USA Adobe Inc San Jose CA USA Rutgers State Univ New Brunswick NJ USA Google Inc Mountain View CA USA Huazhong Univ Sci & Technol Wuhan Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9798350353006

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible.

关键词： Contrastive Learning

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