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EvDiG: Event-guided Direct and Global Components Separation

EvDiG: Event-guided Direct and Global Components Separation

引用

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

作者： Zhou, Xinyu Duan, Peiqi Li, Boyu Zhou, Chu Xu, Chao Shi, Boxin Peking Univ Sch Intelligence Sci & Technol Natl Key Lab Gen AI Beijing Peoples R China Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China

ISBN: (纸本)9798350353006

Separating the direct and global components of a scene aids in shape recovery and basic material understanding. Conventional methods capture multiple frames under high frequency illumination patterns or shadows, requiring the scene to keep stationary during the image acquisition process. Single-frame methods simplify the capture procedure but yield lower-quality separation results. In this paper, we leverage the event camera to facilitate the separation of direct and global components, enabling video-rate separation of high quality. In detail, we adopt an event camera to record rapid illumination changes caused by the shadow of a line occluder sweeping over the scene, and reconstruct the coarse separation results through event accumulation. We then design a network to resolve the noise in the coarse separation results and restore color information. A real-world dataset is collected using a hybrid camera system for network training and evaluation. Experimental results show superior performance over state-of-the-art methods.

关键词： direct-global separation event-guided vision

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PeVL: Pose-Enhanced vision-Language Model for Fine-Grained Human Action recognition

PeVL: Pose-Enhanced Vision-Language Model for Fine-Grained H...

引用

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

作者： Zhang, Haosong Leong, Mei Chee Li, Liyuan Lin, Weisi Nanyang Technol Univ Inst Infocomm Res I2R A STAR Singapore Singapore

ISBN: (纸本)9798350353006

Recent progress in vision-Language (VL) foundation models has revealed the great advantages of cross-modality learning. However, due to a large gap between vision and text, they might not be able to sufficiently utilize the benefits of cross-modality information. In the field of human action recognition, the additional pose modality may bridge the gap between vision and text to improve the effectiveness of cross-modality learning. In this paper, we propose a novel framework, called Pose-enhanced vision-Language (PeVL) model, to adapt the VL model with pose modality to learn effective knowledge of fine-grained human actions. Our PeVL model includes two novel components: an Unsymmetrical Cross-Modality Refinement (UCMR) block and a Semantic-Guided Multi-level Contrastive (SGMC) module. The UCMR block includes Pose-guided Visual Refinement (P2V-R) and Visual-enriched Pose Refinement (V2P-R) for effective cross-modality learning. The SGMC module includes Multi-level Contrastive Associations of vision-text and pose-text at both action and sub-action levels, and a Semantic-Guided Loss, enabling effective contrastive learning with text. Built upon a pre-trained VL foundation model, our model integrates trainable adapters and can be trained end-to-end. Our novel PeVL design over VL foundation model yields remarkable performance gains on four fine-grained human action recognition datasets, achieving a new SOTA with a significantly small number of FLOPs for low-cost re-training.(1)

关键词： Semantics

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Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection

Prompt-Enhanced Multiple Instance Learning for Weakly Superv...

引用

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

作者： Chen, Junxi Li, Liang Su, Li Zha, Zheng-Jun Huang, Qingming Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Key Lab Intell Info Proc ICT Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Key Lab Safety Beijing Peoples R China

ISBN: (纸本)9798350353006

Weakly-supervised Video Anomaly Detection (wVAD) aims to detect frame-level anomalies using only video-level labels in training. Due to the limitation of coarse-grained labels, Multi-Instance Learning (MIL) is prevailing in wVAD. However, MIL suffers from insufficiency of binary supervision to model diverse abnormal patterns. Besides, the coupling between abnormality and its context hinders the learning of clear abnormal event boundary. In this paper, we propose prompt-enhanced MIL to detect various abnormal events while ensuring clear event boundaries. Concretely, we design the abnormal-aware prompts by using abnormal class annotations together with learnable prompt, which can incorporate semantic priors into video features dynamically. The detector can utilize the semantic-rich features to capture diverse abnormal patterns. In addition, normal context prompt is introduced to amplify the distinction between abnormality and its context, facilitating the generation of clear boundary. With the mutual enhancement of abnormal-aware and normal context prompt, the model can construct discriminative representations to detect divergent anomalies without ambiguous event boundaries. Extensive experiments demonstrate our method achieves SOTA performance on three public bench-marks. The code is available at https://***/Junxi-Chen/PE-MIL.

关键词： computer vision Video Anomaly Detection

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3D Feature Tracking via Event Camera

3D Feature Tracking via Event Camera

引用

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

作者： Li, Siqi Zhou, Zhikuan Xue, Zhou Li, Yipeng Du, Shaoyi Gao, Yue Tsinghua Univ Sch Software BNRist THUIBCS Beijing Peoples R China Li Auto Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Natl Engn Res Ctr Visual Informat & Applicat Natl Key Lab Human Machine Hybrid Augmented Intel Xian Peoples R China

ISBN: (纸本)9798350353006

This paper presents the first 3D feature tracking method with the corresponding dataset. Our proposed method takes event streams from stereo event cameras as input to predict 3D trajectories of the target features with high-speed motion. To achieve this, our method leverages a joint framework to predict the 2D feature motion offsets and the 3D feature spatial position simultaneously. A motion compensation module is leveraged to overcome the feature deformation. A patch matching module based on bipolarity hypergraph modeling is proposed to robustly estimate the feature spatial position. Meanwhile, we collect the first 3D feature tracking dataset with high-speed moving objects and ground truth 3D feature trajectories at 250 FPS, named E-3DTrack, which can be used as the first high-speed 3D feature tracking benchmark. Our code and dataset could be found at: https:// github. com/ lisiqi19971013/E-3DTrack.

关键词： 3D vision Dataset Event Camera Feature Tracking

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Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action recognition

Align before Adapt: Leveraging Entity-to-Region Alignments f...

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

作者： Chen, Yifei Chen, Dapeng Liu, Ruijin Zhou, Sai Xue, Wenyuan Peng, Wei Huawei Technol IT Innovat & Res Ctr Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Large-scale visual-language pre-trained models have achieved significant success in various video tasks. However, most existing methods follow an adapt then align paradigm, which adapts pre-trained image encoders to model video-level representations and utilizes one-hot or text embedding of the action labels for supervision. This paradigm overlooks the challenge of mapping from static images to complicated activity concepts. In this paper, we propose a novel Align before Adapt (ALT) paradigm. Prior to adapting to video representation learning, we exploit the entity-to-region alignments for each frame. The alignments are fulfilled by matching the region-aware image embeddings to an offline-constructed text corpus. With the aligned entities, we feed their text embeddings to a transformer-based video adapter as the queries, which can help extract the semantics of the most important entities from a video to a vector. This paradigm reuses the visual-language alignment of VLP during adaptation and tries to explain an action by the underlying entities. This helps understand actions by bridging the gap with complex activity semantics, particularly when facing unfamiliar or unseen categories. ALT demonstrates competitive performance while maintaining remarkably low computational costs. In fully supervised experiments, it achieves 88.1 % top-1 accuracy on Kinetics-400 with only 4947 GFLOPs. Moreover, ALT outperforms the previous state-of-the-art methods in both zero-shot and fewshot experiments, emphasizing its superior generalizability across various learning scenarios.

关键词： Video action recognition visual-language model

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Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension

Extend Your Own Correspondences: Unsupervised Distant Point ...

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

作者： Liu, Quan Zhul, Hongzi Wang, Zhenxi Zhou, Yunsong Chang, Shan Guo, Minyi Shanghai Jiao Tong Univ Shanghai Peoples R China Donghua Univ Shanghai Peoples R China

ISBN: (纸本)9798350353006

Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper, we propose EYOC, an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly, requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion, where in each round, the feature extractor, trained with near point cloud pairs, can label slightly farther point cloud pairs, enabling self- supervision on such far point cloud pairs. This process continues until the derived extractor can be used to register distant point clouds. Particularly, to enable high-fidelity correspondence label generation, we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair, and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover, it outwits supervised methods regarding generalization performance on new data distributions.

关键词： 3D vision Autonomous Driving Point Cloud Registration Unsupervised Learning

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360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries

360Loc: A Dataset and Benchmark for Omnidirectional Visual L...

引用

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

作者： Huang, Huajian Liu, Changkun Zhu, Yipeng Cheng, Hui Braud, Tristan Yeung, Sai-Kit Hong Kong Univ Sci & Technol Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9798350353006

Portable 360 degrees cameras are becoming a cheap and efficient tool to establish large visual databases. By capturing omnidirectional views of a scene, these cameras could expedite building environment models that are essential for visual localization. However, such an advantage is often overlooked due to the lack of valuable datasets. This paper introduces a new benchmark dataset, 360Loc, composed of 360 degrees images with ground truth poses for visual localization. We present a practical implementation of 360 degrees mapping combining 360 degrees images with lidar data to generate the ground truth 6DoF poses. 360Loc is the first dataset and benchmark that explores the challenge of cross-device visual positioning, involving 360 degrees reference frames, and query frames from pinhole, ultra-wide FoV fisheye, and 360 degrees cameras. We propose a virtual camera approach to generate lower-FoV query frames from 360 degrees images, which ensures a fair comparison of performance among different query types in visual localization tasks. We also extend this virtual camera approach to feature matching-based and pose regression-based methods to alleviate the performance loss caused by the cross-device domain gap, and evaluate its effectiveness against state-of-the-art baselines. We demonstrate that omnidirectional visual localization is more robust in challenging large-scale scenes with symmetries and repetitive structures. These results provide new insights into 360-camera mapping and omnidirectional visual localization with cross-device queries. Project Page and dataset: https://***/research/360Loc/.

关键词： omnidirectional vision visual lozalization

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Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language

Separating the "Chirp" from the "Chat": Self-supervised Visu...

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

作者： Hamilton, Mark Zisserman, Andrew Hershey, John R. Freeman, William T. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Microsoft Redmond WA 98052 USA Google Oxford England

ISBN: (纸本)9798350353006

We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visual aligned features solely through watching videos. We show that DenseAV can discover the "meaning" of words and the "location" of sounds without explicit localization supervision. Furthermore, it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast, many other systems that learn "global" audio and video representations cannot localize words and sound. Finally, we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the current state-of-the-art, ImageBind, on cross-modal retrieval using fewer than half of the parameters. Project Page: https://***/denseav

关键词： audio audio-visual contrastive learning language multimodal object discovery semantic segmentation sound unsupervised learning vision visual grounding

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Language-aware Visual Semantic Distillation for Video Question Answering

Language-aware Visual Semantic Distillation for Video Questi...

引用

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

作者： Zou, Bo Yang, Chao Qiao, Yu Quan, Chengbin Zhao, Youjian Tsinghua Univ Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Significant progress in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although image-language models can efficiently represent both video and language branches, they typically employ goal-free vision perception and do not interact vision with language well during the answer generation, thus omitting crucial visual cues. In this paper, we are inspired by the human recognition and learning pattern and propose VideoDistill, a framework with language-aware (i.e., goal-driven) behavior in both vision perception and answer generation. VideoDistill generates answers only from question-related visual embeddings and follows a thinking-observing-answering approach that closely resembles human behavior, distinguishing it from previous research. Specifically, we develop a language-aware gating mechanism to replace the standard cross-attention, avoiding language's direct fusion into visual representations. We incorporate this mechanism into two key components of the entire framework. The first component is a differentiable sparse sampling module, which selects frames containing the necessary dynamics and semantics relevant to the questions. The second component is a vision refinement module that merges existing spatial-temporal attention layers to ensure extracting multi-grained visual semantics associated with the questions. We conduct evaluations on various challenging video question-answering benchmarks, and VideoDistill achieves state-of-the-art performance in both general and long-form VideoQA datasets. In Addition, we verify that VideoDistill can effectively alleviate the utilization of language shortcut solutions in the EgoTaskQA dataset.

关键词： Multi-modal Fusion Question-Answering Video Understanding VideoQA vision-Language

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Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes

Rethinking Style Transfer: From Pixels to Parameterized Brus...

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

作者： Kotovenko, Dmytro Wright, Matthias Heimbrecht, Arthur Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc IWR Heidelberg Germany

ISBN: (纸本)9781665445092

There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce a simple differentiable rendering mechanism. Our approach significantly improves visual quality and enables additional control over the stylization process such as controlling the flow of brushstrokes through user input. We provide qualitative and quantitative evaluations that show the efficacy of the proposed parameterized representation. Code is available at https://***/CompVis/brushstroke-parameterizedstyle-transfer.

关键词： Visualization computer vision Codes Process control Rendering (computer graphics) pattern recognition Painting

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