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AffordanceLLM: Grounding Affordance from vision Language Models

AffordanceLLM: Grounding Affordance from Vision Language Mod...

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

作者： Qian, Shengyi Chen, Weifeng Bai, Mm Zhou, Xiong Tu, Zhuowen Li, Li Erran Amazon AWS AI Seattle WA 98109 USA

ISBN: (纸本)9798350365474

Affordance grounding refers to the task of finding the area of an object with which one can interact. It is a fundamental but challenging task, as a successful solution requires the comprehensive understanding of a scene in multiple aspects including detection, localization, and recognition of objects with their parts, of geo-spatial configuration/layout of the scene, of 3D shapes and physics, as well as of the functionality and potential interaction of the objects and humans. Much of the knowledge is hidden and beyond the image content with the supervised labels from a limited training set. In this paper, we make an attempt to improve the generalization capability of the current affordance grounding by taking the advantage of the rich world, abstract, and human-object-interaction knowledge from pre-trained large-scale vision language models [40]. Under the AGD20K benchmark, our proposed model demonstrates a significant performance gain over the competing methods for in-the-wild object affordance grounding. We further demonstrate it can ground affordance for objects from random Internet images, even if both objects and actions are unseen during training.

关键词： Visual languages

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LEOD: Label-Efficient Object Detection for Event Cameras

LEOD: Label-Efficient Object Detection for Event Cameras

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

作者： Wu, Ziyi Gehrig, Mathias Lyu, Qing Liu, Xudong Gilitschenski, Igor Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada Univ Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However, it is costly to fully label event streams for supervised training due to their high temporal resolution. To reduce this cost, we present LEOD, the first method for label-efficient event-based detection. Our approach unifies weakly- and semi-supervised object detection with a self-training mechanism. We first utilize a detector pre-trained on limited labels to produce pseudo ground truth on unlabeled events. Then, the detector is re-trained with both real and generated labels. Leveraging the temporal consistency of events, we run bi-directional inference and apply tracking-based post-processing to enhance the quality of pseudo labels. To stabilize training against label noise, we further design a soft anchor assignment strategy. We introduce new experimental protocols to evaluate the task of label-efficient event-based detection on Gen1 and 1Mpx datasets. LEOD consistently outperforms supervised baselines across various labeling ratios. For example, on Gen1, it improves mAP by 8.6% and 7.8% for RVT-S trained with 1% and 2% labels. On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels. LEOD maintains its effectiveness even when all labeled data are available, reaching new state-of-the-art results. Finally, we show that our method readily scales to improve larger detectors as well. Code: https://***/Wuziyi616/LEOD.

关键词： Event-based vision Object Detection Semi-supervised Learning Weakly-supervised Learning

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HHMR: Holistic Hand Mesh Recovery by Enhancing the Multimodal Controllability of Graph Diffusion Models

HHMR: Holistic Hand Mesh Recovery by Enhancing the Multimoda...

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

作者： Li, Mengcheng Zhang, Hongwen Zhang, Yuxiang Shao, Ruizhi Yu, Tao Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recent years have witnessed a trend of the deep integration of the generation and reconstruction paradigms. In this paper, we extend the ability of controllable generative models for a more comprehensive hand mesh recovery task: direct hand mesh generation, inpainting, reconstruction, and fitting in a single framework, which we name as Holistic Hand Mesh Recovery (HHMR). Our key observation is that different kinds of hand mesh recovery tasks can be achieved by a single generative model with strong multimodal controllability, and in such a framework, realizing different tasks only requires giving different signals as conditions. To achieve this goal, we propose an all-in-one diffusion framework based on graph convolution and attention mechanisms for holistic hand mesh recovery. In order to achieve strong control generation capability while ensuring the decoupling of multimodal control signals, we map different modalities to a shared feature space and apply cross-scale random masking in both modality and feature levels. In this way, the correlation between different modalities can be fully exploited during the learning of hand priors. Furthermore, we propose Condition-aligned Gradient Guidance to enhance the alignment of the generated model with the control signals, which significantly improves the accuracy of the hand mesh reconstruction and fitting. Experiments show that our novel framework can realize multiple hand mesh recovery tasks simultaneously and outperform the existing methods in different tasks, which provides more possibilities for subsequent downstream applications including gesture recognition, pose generation, mesh editing, and so on.

关键词： Gesture recognition

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No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation

No Time to Train: Empowering Non-Parametric Networks for Few...

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

作者： Zhu, Xiangyang Zhang, Renrui He, Bowei Guo, Ziyu Liu, Jiaming Xiao, Han Fu, Chaoyou Dong, Hao Gao, Peng City Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Peking Univ Beijing Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes, and then evaluate their generalization performance on 'unseen' classes. However, the prior pre-training stage not only introduces excessive time over-head but also incurs a significant domain gap on 'unseen' classes. To tackle these issues, we propose a Non-parametric Network for few-shot 3D Segmentation, SegNN, and its Parametric variant, Seg-PN. Without training, Seg-NN extracts dense representations by hand-crafted filters and achieves comparable performance to existing parametric models. Due to the elimination of pre-training, SegNN can alleviate the domain gap issue and save a substantial amount of time. Based on Seg-NN, Seg-PN only requires training a lightweight QUEry-Support Transferring (QUEST) module, which enhances the interaction between the support set and query set. Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively, while reducing training time by -90%, indicating its effectiveness and efficiency. Code is available here.

关键词： 3D Segmentation 3D vision Few-shot Learning

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Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge

Teeth-SEG: An Efficient Instance Segmentation Framework for ...

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

作者： Zou, Bo Wang, Shaofeng Liu, Hao Sun, Gaoyue Wang, Yajie Zuo, FeiFei Quan, Chengbin Zhaot, Youjian Tsinghua Univ Beijing Peoples R China Capital Med Univ Beijing Peoples R China Imperial Coll London London England LargeV Inc Beijing Peoples R China Tsinghua Univ Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Teeth localization, segmentation, and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes ( e.g., maxillary first premolar and second premolar), 2) the teeth's position and shape variation across subjects, and 3) the presence of abnormalities in the dentition (e.g., caries and edentulism). To address these problems, we propose a ViT-based framework named TeethSEG, which consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer. Specifically, to compose the two modules, we design a unique permutation-based upscaler to ensure high efficiency while establishing clear segmentation boundaries with multi-head self/cross-gating layers to emphasize particular semantics meanwhile maintaining the divergence between token embeddings. Besides, we collect the first open-sourced intraoral image dataset IO150K, which comprises over 150k intraoral photos, and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental image segmentation.

关键词： Image Segmentation Intra-Oral Image Medical vision Teeth Segmentation

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Latent-based Diffusion Model for Long-tailed recognition

Latent-based Diffusion Model for Long-tailed Recognition

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

作者： Han, Pengxiao Ye, Changkun Zhou, Jieming Zhang, Jing Hong, Jie Li, Xuesong Australian Natl Univ Canberra ACT Australia CSIRO Canberra ACT Australia

ISBN: (纸本)9798350365474

Long-tailed imbalance distribution is a common issue in practical computer vision applications. Previous works proposed methods to address this problem, which can be categorized into several classes: re-sampling, re-weighting, transfer learning, and feature augmentation. In recent years, diffusion models have shown an impressive generation ability in many sub-problems of deep computer vision. However, its powerful generation has not been explored in long-tailed problems. We propose a new approach, the Latent-based Diffusion Model for Long-tailed recognition (LDMLR), as a feature augmentation method to tackle the issue. First, we encode the imbalanced dataset into features using the baseline model. Then, we train a Denoising Diffusion Implicit Model (DDIM) using these encoded features to generate pseudo-features. Finally, we train the classifier using the encoded and pseudo-features from the previous two steps. The model's accuracy shows an improvement on the CIFAR-LT and ImageNet-LT datasets by using the proposed method.

关键词： diffusion model imbalance distribution long-tailed recognition

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ES³: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations

ES<SUP>3</SUP>: Evolving Self-Supervised Learning of Robust ...

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

作者： Zhang, Yuanhang Yang, Shuang Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9798350353006

We propose a novel strategy, ES3, for self-supervised learning of robust audio-visual speech representations from unlabeled talking face videos. While many recent approaches for this task primarily rely on guiding the learning process using the audio modality alone to capture information shared between audio and video, we reframe the problem as the acquisition of shared, unique (modality-specific) and synergistic speech information to address the inherent asymmetry between the modalities. Based on this formulation, we propose a novel "evolving" strategy that progressively builds joint audio-visual speech representations that are strong for both uni-modal (audio & visual) and bi-modal (audio-visual) speech. First, we leverage the more easily learnable audio modality to initialize audio and visual representations by capturing audio-unique and shared speech information. Next, we incorporate video-unique speech information and bootstrap the audio-visual representations on top of the previously acquired shared knowledge. Finally, we maximize the total audio-visual speech information, including synergistic information to obtain robust and comprehensive representations. We implement ES3 as a simple Siamese framework and experiments on both English benchmarks and a newly contributed large-scale Mandarin dataset show its effectiveness. In particular, on LRS2-BBC, our smallest model is on par with SoTA models with only 1/2 parameters and 1/8 unlabeled data (223h).

关键词： audio-visual speech representations multi-modal learning representation learning self-supervised learning speech recognition

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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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3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos

3DGStream: On-the-Fly Training of 3D Gaussians for Efficient...

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

作者： Sun, Jiakai Jiao, Han Li, Guangyuan Zhang, Zhanjie Zhao, Lei Xing, Wei Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Constructing photo-realistic Free-Viewpoint Videos ( FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor. Despite the remarkable advance-ments achieved by current neural rendering techniques, these methods generally require complete video sequences for offline training and are not capable of real-time render-ing. To address these constraints, we introduce 3DGStream, a method designed for efficient FVV streaming of real-world dynamic scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12 seconds and real-time rendering at 200 FPS. Specifically, we utilize 3D Gaussians ( 3DGs) to represent the scene. Instead of the naive ap-proach of directly optimizing 3DGs per-frame, we employ a compact Neural Transformation Cache (NTC) to model the translations and rotations of 3DGs, markedly reducing the training time and storage required for each FVV frame. Furthermore, we propose an adaptive 3DG addition strat-egy to handle emerging objects in dynamic scenes. Exper-iments demonstrate that 3DGStream achieves competitive performance in terms of rendering speed, image quality, training time, and model storage when compared with state-of-the-art methods.

关键词： 3D Gaussian Splatting 3D vision Dynamic Scene Reconstruction Free-Viewpoint Video Streaming Media

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Three Pillars improving vision Foundation Model Distillation for Lidar

Three Pillars improving Vision Foundation Model Distillation...

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

作者： Puy, Gilles Gidaris, Spyros Boulch, Alexandre Simeoni, Oriane Sautier, Corentin Perez, Patrick Bursucl, Andrei Marlet, Renaud Valeo ai Paris France Kyutai Paris France Univ Gustave Eiffel CNRS LIGM Ecole Ponts Marne La Vallee France

ISBN: (纸本)9798350353006

Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervision. Ideally, 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results, obtained thanks to distillation methods that keep improving. Yet, we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work, instead of focusing only on the distillation method, we study the effect of three pillars for distillation: the 3D backbone, the pretrained 2D backbone, and the pretraining 2D+3D dataset. In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features, and to improve the robustness of the pretrained backbones to domain gaps and perturbations. The code is available at https://***/valeoai/ScaLR.

关键词： Semantic Segmentation

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