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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...

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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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EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object recognition

EventDance: Unsupervised Source-free Cross-modal Adaptation ...

引用

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

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

ISBN: (纸本)9798350353006

In this paper, we make the first attempt at achieving the cross-modal (i.e., image-to-events) adaptation for event-based object recognition without accessing any labeled source image data owning to privacy and commercial issues. Tackling this novel problem is non-trivial due to the novelty of event cameras and the distinct modality gap between images and events. In particular, as only the source model is available, a hurdle is how to extract the knowledge from the source model by only using the unlabeled target event data while achieving knowledge transfer. To this end, we propose a novel framework, dubbed EventDance for this unsupervised source-free cross-modal adaptation problem. Importantly, inspired by event-to-video reconstruction methods, we propose a reconstruction-based modality bridging (RMB) module, which reconstructs intensity frames from events in a self-supervised manner. This makes it possible to build up the surrogate images to extract the knowledge (i.e., labels) from the source model. We then propose a multi-representation knowledge adaptation ( MKA) module that transfers the knowledge to target models learning events with multiple representation types for fully exploring the spatiotemporal information of events. The two modules connecting the source and target models are mutually updated so as to achieve the best performance. Experiments on three benchmark datasets with two adaption settings show that EventDance is on par with prior methods utilizing the source data.

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Contextual Augmented Global Contrast for Multimodal Intent recognition

Contextual Augmented Global Contrast for Multimodal Intent R...

引用

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

作者： Sun, Kaili Xie, Zhiwen Ye, Mang Zhang, Huyin Wuhan Univ Sch Comp Sci Wuhan Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9798350353006

Multimodal intent recognition (MIR) aims to perceive the human intent polarity via language, visual, and acoustic modalities. The inherent intent ambiguity makes it challenging to recognize in multimodal scenarios. Existing MIR methods tend to model the individual video independently, ignoring global contextual information across videos. This learning manner inevitably introduces perception biases, exacerbated by the inconsistencies of the multimodal representation, amplifying the intent uncertainty. This challenge motivates us to explore effective global context modeling. Thus, we propose a context-augmented global contrast (CAGC) method to capture rich global context features by mining both intra-and cross-video context interactions for MIR. Concretely, we design a context-augmented transformer module to extract global context dependencies across videos. To further alleviate error accumulation and interference, we develop a cross-video bank that retrieves effective video sources by considering both intentional tendency and video similarity. Furthermore, we introduce a global context-guided contrastive learning scheme, designed to mitigate inconsistencies arising from global context and individual modalities in different feature spaces. This scheme incorporates global cues as the supervision to capture robust the multimodal intent representation. Experiments demonstrate CAGC obtains superior performance than state-of-the-art MIR methods. We also generalize our approach to a closely related task, multimodal sentiment analysis, achieving the comparable performance.

关键词： Contrastive learning Global contextual information Multimodal intent recognition

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HyperLeaf2024-A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

HyperLeaf2024-A Hyperspectral Imaging Dataset for Classifica...

引用

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

作者： Laprade, William Michael Pieta, Pawel Kutuzova, Svetlana Westergaard, Jesper Cairo Nielsen, Mads Christensen, Svend Dahl, Anders Bjorholm Tech Univ Denmark Dept Appl Math & Comp Sci Copenhagen Denmark Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Dept Plant & Environm Sci Copenhagen Denmark

ISBN: (纸本)9798350365474

Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellitebased land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on perpixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in wholeimage hyperspectral image analysis, we introduce the HyperLeaf2024 dataset. The dataset consists of 2410 hyperspectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyperspectral image analysis.

关键词： dataset deep learning hyperspectral imaging plant science

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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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Benchmarking Zero-Shot recognition with vision-Language Models: Challenges on Granularity and Specificity

Benchmarking Zero-Shot Recognition with Vision-Language Mode...

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

作者： Xu, Zhenlin Zhu, Yi Deng, Siqi Mittal, Abhay Chen, Yanbei Wang, Manchen Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Seattle WA 98109 USA Boson AI Santa Clara CA 95054 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350365474

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs' consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn't fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.

关键词： Benchmarking Foundational Models vision and language models Zero-shot recognition

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Progressive Semantic-Guided vision Transformer for Zero-Shot Learning

Progressive Semantic-Guided Vision Transformer for Zero-Shot...

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

作者： Chen, Shiming Hou, Wenjin Khan, Salman Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Huazhong Univ Sci & Technol Wuhan Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information. Specifically, we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets, i.e., CUB, SUN, and AWA2.

关键词： zero-shot learning

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RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding

RegionPLC: Regional Point-Language Contrastive Learning for ...

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

作者： Yang, Jihan Ding, Runyu Deng, Weipeng Wang, Zhe Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350353006

We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely RegionPLC, for open-world 3D scene understanding, aiming to identify and recognize open-set objects and categories. Specifically, based on our empirical studies, we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models, yielding high-quality, dense region-level language descriptions without human 3D annotations. Subsequently, we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet, ScanNet200, and nuScenes datasets, and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation, respectively, while maintaining greater scalability and lower resource demands. Furthermore, our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code will be released at github.

关键词： 3D vision embodied AI Open-world

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CAFF-DINO: Multi-spectral object detection transformers with cross-attention features fusion

CAFF-DINO: Multi-spectral object detection transformers with...

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

作者： Helvig, Kevin Abeloos, Baptiste Trouve-Peloux, Pauline Univ Paris Saclay ONERA DTIS F-91120 Palaiseau France

ISBN: (纸本)9798350365474

Object detection on images can find benefit from coupling multiple spectra, each presenting specific useful features. However, building an efficient architecture coupling the different modalities is a complex task. Transformers, due to their ability to extract meaningful correlations between the different regions of the inputs appear as a promising way to perform features fusion across different spectra. This work presents a multi-spectral object detection architecture based on cross-attention features fusion (CAFF), combined with a transformer based detector (DINO). We demonstrate here the performance of the proposed approach in object detection compared with state-of-the-art approaches, on infrared-visible multi-spectral datasets. Moreover the robustness to systematic misalignment between image pairs is studied. The proposed approach is generic to any mono-spectrum transformer based detectors. The model developed in this study will be available in a dedicated github repository.

关键词： Object recognition

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Enhancing Emotion recognition with Pre-trained Masked Autoencoders and Sequential Learning

Enhancing Emotion Recognition with Pre-trained Masked Autoen...

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

作者： Zhou, Weiwei Lu, Jiada Ling, Chenkun Wang, Weifeng Liu, Shaowei Chinatelecom Cloud Chengdu Peoples R China

ISBN: (纸本)9798350365474

Human emotion recognition plays a pivotal role in facilitating seamless interactions between humans and computers. This paper delineates our methodology in tackling the Valence-Arousal (VA) Estimation Challenge, Expression (Expr) recognition Challenge, and Action Unit (AU) Detection Challenge within the ambit of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). Our study advocates a novel approach aimed at refining continuous emotion recognition. We achieve this by first pre-training with Masked Autoencoders (MAE) on facial datasets and then fine-tuning the model on the affwild2 dataset, which is annotated with expression (Expr) labels. The pre-trained model serves as an adept visual feature extractor, thereby enhancing the model's robustness. Furthermore, we bolster the performance of continuous emotion recognition by integrating Temporal Convolutional Network (TCN) modules and Transformer Encoder modules into our framework. Our model excels beyond baseline performance, securing a commendable 3rd place in the Valence-Arousal Estimation Challenge, while also achieving an impressive 2nd place in both the Expression recognition Challenge and the Action Unit Detection Challenge.

关键词： Emotion recognition

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