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Part-aware Unified Representation of Language and Skeleton for Zero-shot Action recognition

Part-aware Unified Representation of Language and Skeleton f...

引用

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

作者： Zhu, Anqi Ke, Qiuhong Gong, Mingming Bailey, James Univ Melbourne Melbourne Vic Australia Monash Univ Parkville Vic 3052 Australia Wellington Rd Clayton Vic 3800 Australia

ISBN: (纸本)9798350353006

While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored. In this paper, we argue that relying solely on aligning label-level semantics and global skeleton features is insufficient to effectively transfer locally consistent visual knowledge from seen to unseen classes. To address this limitation, we introduce Part-aware Unified Representation between Language and Skeleton (PURLS) to explore visual-semantic alignment at both local and global scales. PURLS introduces a new prompting module and a novel partitioning module to generate aligned textual and visual representations across different levels. The former leverages a pre-trained GPT-3 to infer refined descriptions of the global and local (body-part-based and temporal-interval-based) movements from the original action labels. The latter employs an adaptive sampling strategy to group visual features from all body joint movements that are semantically relevant to a given description. Our approach is evaluated on various skeleton/language backbones and three large-scale datasets, i.e., NTU-RGB+D 60, NTU-RGB+D 120, and a newly curated dataset Kinetics-skeleton 200. The results showcase the universality and superior performance of PURLS, surpassing prior skeleton-based solutions and standard baselines from other domains. The source codes can be accessed at https://***/azzh1/PURLS.

关键词： action recognition computer vision contrastive learning large language model representation learning skeleton action recognition zero-shot learning

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Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

引用

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

作者： Sun, Zeyi Fang, Ye Wu, Tong Zhang, Pan Zang, Yuhang Kong, Shu Xiong, Yuanjun Lin, Dahua Wang, Jiaqi Shanghai Jiao Tong Univ Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Macau Taipa Macao Peoples R China MThreads Inc Beijing Peoples R China

ISBN: (纸本)9798350353006

Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual modalities to comprehend the entire image, including all the details, even those irrelevant to specific tasks. However, for a finer understanding and controlled editing of images, it becomes crucial to focus on specific regions of interest, which can be indicated as points, masks, or boxes by humans or perception models. To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs. Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents. It demonstrates effectiveness in various tasks, including but not limited to open-world recognition, multimodal large language models, and conditional 2D / 3D generation. It has a strong potential to serve as a versatile tool for image-related tasks. Our project is with codes and models available is linked to https://***/alpha-clip/.

关键词： CLIP MLLMs vision-language pretraining

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MAPLM: A Real-World Large-Scale vision-Language Benchmark for Map and Traffic Scene Understanding

MAPLM: A Real-World Large-Scale Vision-Language Benchmark fo...

引用

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

作者： Cao, Xu Zhou, Tong Ma, Yunsheng Ye, Wenqian Cui, Can Tang, Kun Cao, Zhipeng Liang, Kaizhao Wang, Ziran Rehg, James M. Zheng, Chao Tencent T Lab Palo Alto CA 94306 USA Univ Illinois Champaign IL USA Purdue Univ W Lafayette IN USA Univ Virginia Charlottesville VA USA SambaNova Syst Inc Palo Alto CA USA

ISBN: (纸本)9798350353006

vision-language generative AI has demonstrated remarkable promise for empowering cross-modal scene understanding of autonomous driving and high-definition (HD) map systems. However, current benchmark datasets lack multi-modal point cloud, image, and language data pairs. Recent approaches utilize visual instruction learning and cross-modal prompt engineering to expand vision-language models into this domain. In this paper, we propose a new vision-language benchmark that can be used to finetune traffic and HD map domain-specific foundation models. Specifically, we annotate and leverage large-scale, broad-coverage traffic and map data extracted from huge HD map annotations, and use CLIP and LLaMA-2 / Vicuna to finetune a baseline model with instruction-following data. Our experimental results across various algorithms reveal that while visual instruction-tuning large language models (LLMs) can effectively learn meaningful representations from MAPLM-QA, there remains significant room for further advancements. To facilitate applying LLMs and multi-modal data into self-driving research, we will release our visual-language QA data, and the baseline models at ***/LLVM-AD/MAPLM.

关键词： High-definition (HD) Map Large Language Model Multimodal Learning vision-Language Model Visual Question Answering

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JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments

JRDB-PanoTrack: An Open-world Panoptic Segmentation and Trac...

引用

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

作者： Lel, Duy Tho Gou, Chenhui Datta, Stavya Shi, Hengcan Reid, Ian Cai, Jianfei Rezatofighi, Hamid Monash Univ Melbourne Vic Australia MBZUAI Abu Dhabi U Arab Emirates Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350353006

Autonomous robot systems have attracted increasing research attention in recent years, where environment understanding is a crucial step for robot navigation, human-robot interaction, and decision. Real-world robot systems usually collect visual data from multiple sensors and are required to recognize numerous objects and their movements in complex human-crowded settings. Traditional benchmarks, with their reliance on single sensors and limited object classes and scenarios, fail to provide the comprehensive environmental understanding robots need for accurate navigation, interaction, and decision-making. As an extension of JRDB dataset, we unveil JRDB-PanoTrack, a novel open-world panoptic segmentation and tracking benchmark, towards more comprehensive environmental perception. JRDB-PanoTrack includes (1) various data involving indoor and outdoor crowded scenes, as well as comprehensive 2D and 3D synchronized data modalities;(2) high-quality 2D spatial panoptic segmentation and temporal tracking annotations, with additional 3D label projections for further spatial understanding;(3) diverse object classes for closed- and open-world recognition benchmarks, with OSPA-based metrics for evaluation. Extensive evaluation of leading methods shows significant challenges posed by our dataset.

关键词： computer vision Open World Panoptic Segmentation Panoptic Tracking Robotic

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VideoCon: Robust Video-Language Alignment via Contrast Captions

VideoCon: Robust Video-Language Alignment via Contrast Capti...

引用

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

作者： Bansall, Hritik Bitton, Yonatan Szpektor, Idan Chang, Kai-Wei Grover, Aditya UCLA Los Angeles CA 90095 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments, such as replacing entities, actions, and flipping event order, which alignment models should be robust against. To this end, we introduce the VideoCon, a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then, a generative video-language model is fine-tuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally, our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover, our model shows superior performance on novel videos and human-crafted captions and explanations.

关键词： alignment explanation Language large multimodal models Multimodal Video Video-Language vision-language

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Differentiable Shadow Mapping for Efficient Inverse Graphics

Differentiable Shadow Mapping for Efficient Inverse Graphics

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

作者： Worchel, Markus Alexa, Marc TU Berlin Berlin Germany

ISBN: (纸本)9798350301298

We show how shadows can be efficiently generated in differentiable rendering of triangle meshes. Our central observation is that pre-filtered shadow mapping, a technique for approximating shadows based on rendering from the perspective of a light, can be combined with existing differentiable rasterizers to yield differentiable visibility information. We demonstrate at several inverse graphics problems that differentiable shadow maps are orders of magnitude faster than differentiable light transport simulation with similar accuracy - while differentiable rasterization without shadows often fails to converge.

关键词： vision + graphics

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ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action recognition and More

ExACT: Language-guided Conceptual Reasoning and Uncertainty ...

引用

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

作者： Zhou, Jiazhou Zheng, Xu Lyu, Yuanhuiyi Wang, Lin HKUST GZ AI Thrust Guangzhou Peoples R China HKUST Dept CSE Guangzhou Peoples R China

ISBN: (纸本)9798350353006

Event cameras have recently been shown beneficial for practical vision tasks, such as action recognition, thanks to their high temporal resolution, power efficiency, and reduced privacy concerns. However, current research is hindered by 1) the difficulty in processing events because of their prolonged duration and dynamic actions with complex and ambiguous semantics and 2) the redundant action depiction of the event frame representation with fixed stacks. We find language naturally conveys abundant semantic information, rendering it stunningly superior in reducing semantic uncertainty. In light of this, we propose ExACT, a novel approach that, for the first time, tackles event-based action recognition from a cross-modal conceptualizing perspective. Our ExACT brings two technical contributions. Firstly, we propose an adaptive fine-grained event (AFE) representation to adaptively filter out the repeated events for the stationary objects while preserving dynamic ones. This subtly enhances the performance of ExACT without extra computational cost. Then, we propose a conceptual reasoning-based uncertainty estimation module, which simulates the recognition process to enrich the semantic representation. In particular, conceptual reasoning builds the temporal relation based on the action semantics, and uncertainty estimation tackles the semantic uncertainty of actions based on the distributional representation. Experiments show that our ExACT achieves superior recognition accuracy of 94.83%(+2.23%), 90.10%(+37.47%) and 67.24% on PAF, HARDVS and our SeAct datasets respectively.

关键词： Action recognition Event Camera Language-guided Uncertainty Estimation

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Differentiable Display Photometric Stereo

Differentiable Display Photometric Stereo

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

作者： Choi, Seokjun Yoon, Seungwoo Nam, Giljoo Lee, Seungyong Baek, Seung-Hwan POSTECH Pohang South Korea Meta Real Labs Menlo Pk CA USA

ISBN: (纸本)9798350353006

Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. Display photometric stereo, which employs a conventional monitor as an illumination source, has the potential to overcome limitations often encountered in bulky and difficult-to-use conventional setups. In this paper, we present differentiable display photometric stereo (DDPS), addressing an often over-looked challenge in display photometric stereo: the design of display patterns. Departing from using heuristic display patterns, DDPS learns the display patterns that yield accurate normal reconstruction for a target system in an end-to-end manner. To this end, we propose a differentiable framework that couples basis-illumination image formation with analytic photometric-stereo reconstruction. The differentiable framework facilitates the effective learning of display patterns via auto-differentiation. Also, for training supervision, we propose to use 3D printing for creating a real-world training dataset, enabling accurate reconstruction on the target real-world setup. Finally, we exploit that conventional LCD monitors emit polarized light, which allows for the optical separation of diffuse and specular reflections when combined with a polarization camera, leading to accurate normal reconstruction. Extensive evaluation of DDPS shows improved normal-reconstruction accuracy compared to heuristic patterns and demonstrates compelling properties such as robustness to pattern initialization, calibration errors, and simplifications in image formation and reconstruction.

关键词： Computational illumination computer graphics computer vision Photometric stereo

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SuperLoRA: Parameter-Efficient Unified Adaptation for Large vision Models

SuperLoRA: Parameter-Efficient Unified Adaptation for Large ...

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

作者： Chen, Xiangyu Liu, Jing Wang, Ye Wang, Pu (Perry) Brand, Matthew Wang, Guanghui Koike-Akino, Toshiaki Univ Kansas Lawrence KS 66045 USA Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA Toronto Metropolitan Univ Toronto ON M5B 2K3 Canada

ISBN: (纸本)9798350365474

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance, with up to 10-fold gain in parameter efficiency for transfer learning tasks.

关键词： low-rank adaptation parameter efficiency

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Atom-Level Optical Chemical Structure recognition with Limited Supervision

Atom-Level Optical Chemical Structure Recognition with Limit...

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

作者： Oldenhof, Martijn De Brouwer, Edward Arany, Adam Moreau, Yves Katholieke Univ Leuven ESAT STADIUS Leuven Belgium Yale Univ New Haven CT USA

ISBN: (纸本)9798350353006

Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do not typically generalize well, and show diminished effectiveness when confronted with domains where data is sparse, or costly to generate, such as hand-drawn molecule images. To address this limitation, we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches, our method provides atom-level localization, and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking, we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency, accuracy, and atom-level entity prediction.

关键词： graph recognition object detection OCSR weak supervision

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