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共 21008 条记录，以下是521-530 订阅

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PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization

PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera S...

引用

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

作者： Cai, Yanlu Zhang, Weizhong Wu, Yuan Jin, Cheng Fudan Univ Shanghai Peoples R China Haina Lab Shanghai Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Camera-parameter-free multi-view pose estimation is an emerging technique for 3D human pose estimation (HPE). They can infer the camera settings implicitly or explicitly to mitigate the depth uncertainty impact, showcasing significant potential in real applications. However, due to the limited camera setting diversity in the available datasets, the inferred camera parameters are always simply hard-coded into the model during training and not adaptable to the input in inference, making the learned models cannot generalize well under unseen camera settings. A natural solution is to artificially synthesize some samples, i.e., 2D-3D pose pairs, under massive new camera settings. Unfortunately, to prevent over-fitting the existing camera setting, the number of synthesized samples for each new camera setting should be comparable with that for the existing one, which multiplies the scale of training and even makes it computationally prohibitive. In this paper, we propose a novel HPE approach under the invariant risk minimization (IRM) paradigm. Precisely, we first synthesize 2D poses from myriad camera settings. We then train our model under the IRM paradigm, which targets at learning a common optimal model across all camera settings and thus enforces the model to automatically learn the camera parameters based on the input data. This allows the model to accurately infer 3D poses on unseen data by training on only a handful of samples from each synthesized setting and thus avoid the unbearable training cost increment. Another appealing feature of our method is that benefited from the capability of IRM in identifying the invariant features, its performance on the seen camera settings is enhanced as well. Comprehensive experiments verify the superiority of our approach.

关键词： computer vision Multi-View Pose Estimation

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Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld

Embodied Multi-Modal Agent trained by an LLM from a Parallel...

引用

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

作者： Yang, Yijun Zhou, Tianyi Li, Kanxue Tao, Dapeng Li, Lusong Shen, Li He, Xiaodong Jiang, Jing Shi, Yuhui Southern Univ Sci & Technol Shenzhen Peoples R China Univ Maryland College Pk MD 20742 USA Yunnan Univ Kunming Yunnan Peoples R China JD Explore Acad Beijing Peoples R China Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9798350353006

While large language models (LLMs) excel in a simulated world of texts, they struggle to interact with the more realistic world without perceptions of other modalities such as visual or audio signals. Although vision-language models (VLMs) integrate LLM modules (1) aligned with static image features, and (2) may possess prior knowledge of world dynamics (as demonstrated in the text world), they have not been trained in an embodied visual world and thus cannot align with its dynamics. On the other hand, training an embodied agent in a noisy visual world without expert guidance is often challenging and inefficient. In this paper, we train a VLM agent living in a visual world using an LLM agent excelling in a parallel text world. Specifically, we distill LLM's reflection outcomes (improved actions by analyzing mistakes) in a text world's tasks to finetune the VLM on the same tasks of the visual world, resulting in an Embodied Multi-Modal Agent (EMMA) quickly adapting to the visual world dynamics. Such cross-modality imitation learning between the two parallel worlds is achieved by a novel DAgger-DPO algorithm, enabling EMMA to generalize to a broad scope of new tasks without any further guidance from the LLM expert. Extensive evaluations on the ALFWorld benchmark's diverse tasks highlight EMMA's superior performance to SOTA VLM-based agents, e.g., 20%-70% improvement in the success rate.

关键词： Embodied AI Imitation Learning Large Language Models vision Language Models

来源：评论

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Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

引用

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

作者： Cieslak, Mikolaj Govindarajan, Umabharathi Garcia, Alejandro Chandrashekar, Anuradha Haedrich, Torsten Mendoza-Drosik, Aleksander Michels, Dominik L. Pirk, Soeren Fu, Chia-Chun Palubicki, Wojciech GreenMatterAI Berlin Germany Blue River Technol Santa Clara CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Tech Univ Darmstadt Darmstadt Germany Christian Albrecht Univ Kiel Kiel Germany Adam Mickiewicz Univ Poznan Poland

ISBN: (纸本)9798350365474

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

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BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in vision-based Roadside 3D Object Detection

BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Represen...

引用

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

作者： Wang, Wenjie Lu, Yehao Zheng, Guangcong Zhan, Shuigen Ye, Xiaoqing Tan, Zichang Wang, Jingdong Wang, Gaoang Li, Xi Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ Polytech Inst Hangzhou Zhejiang Peoples R China Baidu Beijing Peoples R China Zhejiang Singapore Innovat & AI Joint Res Lab Singapore Singapore

ISBN: (纸本)9798350353006

vision-based roadside 3D object detection has attracted rising attention in autonomous driving domain, since it encompasses inherent advantages in reducing blind spots and expanding perception range. While previous work mainly focuses on accurately estimating depth or height for 2D- to-3D mapping, ignoring the position approximation error in the voxel pooling process. Inspired by this insight, we propose a novel voxel pooling strategy to reduce such error, dubbed BEVSpread. Specifically, instead of bringing the image features contained in a frustum point to a single BEV grid, BEVSpread considers each frustum point as a source and spreads the image features to the surrounding BEV grids with adaptive weights. To achieve superior propagation performance, a specific weight function is designed to dynamically control the decay speed of the weights according to distance and depth. Aided by customized CUDA parallel acceleration, BEVSpread achieves comparable inference time as the original voxel pooling. Extensive experiments on two large-scale roadside benchmarks demonstrate that, as a plug-in, BEVSpread can significantly improve the performance of existing frustum-based BEV methods by a large margin of (1.12, 5.26, 3.01) AP in vehicle, pedestrian and cyclist. The source code will be made publicly available at BEVSpread.

关键词： 3D Object Detection Autonomous Driving BEV

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How you feelin'? Learning Emotions and Mental States in Movie Scenes

How you feelin'? Learning Emotions and Mental States in Movi...

引用

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

作者： Srivastava, Dhruv Singh, Aditya Kumar Tapaswi, Makarand IIIT Hyderabad CVIT Hyderabad Telangana India

ISBN: (纸本)9798350301298

Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset [72], we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted state-of-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx's self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues.

关键词： language reasoning vision

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Learning Bottleneck Concepts in Image Classification

Learning Bottleneck Concepts in Image Classification

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

作者： Wang, Bowen Li, Liangzhi Nakashima, Lizyuta Nagahara, Hajime Osaka Univ Osaka Japan

ISBN: (纸本)9798350301298

Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel relevance to the decision. Yet, interpreting such explanations may require expert knowledge. Some recent attempts toward interpretability adopt a concept-based framework, giving a higher-level relationship between some concepts and model decisions. This paper proposes Bottleneck Concept Learner (BotCL), which represents an image solely by the presence/absence of concepts learned through training over the target task without explicit supervision over the concepts. It uses self-supervision and tailored regularizers so that learned concepts can be human-understandable. Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability.

关键词： Explainable computer vision

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LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning

LL3DA: Visual Interactive Instruction Tuning for Omni-3D Und...

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

作者： Chen, Sijin Chen, Xin Zhang, Chi Li, Mingsheng Yu, Gang Fei, Hao Zhu, Hongyuan Fan, Jiayuan Chen, Tao Fudan Univ Shanghai Peoples R China Tencent PCG Shenzhen Peoples R China Natl Univ Singapore Singapore Singapore ASTAR Inst InfoComm Res I2R Singapore Singapore ASTAR Ctr Frontier AI Res CFAR Singapore Singapore

ISBN: (纸本)9798350353006

Recent progress in Large Multimodal Models (LMM) has opened up great possibilities for various applications in the field of human-machine interactions. However, developing LMMs that can comprehend, reason, and plan in complex and diverse 3D environments remains a challenging topic, especially considering the demand for understanding permutation-invariant point cloud representations of the 3D scene. Existing works seek help from multi-view images by projecting 2D features to 3D space, which inevitably leads to huge computational overhead and performance degradation. In this paper, we present LL3DA, a Large Language 3D Assistant that takes point cloud as the direct input and responds to both text instructions and visual interactions. The additional visual interaction enables LMMs to better comprehend human interactions with the 3D environment and further remove the ambiguities within plain texts. Experiments show that LL3DA achieves remarkable results and surpasses various 3D vision-language models on both 3D Dense Captioning and 3D Question Answering.

关键词： large language models Multi-modal learning vision and language

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Adversarial Counterfactual Visual Explanations

Adversarial Counterfactual Visual Explanations

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

作者： Jeanneret, Guillaume Simon, Loic Jurie, Frederic Univ Caen Normandie ENSICAEN CNRS Caen France

ISBN: (纸本)9798350301298

Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks cannot be used directly in a counterfactual explanation perspective, as such perturbations are perceived as noise and not as actionable and understandable image modifications. Building on the robust learning literature, this paper proposes an elegant method to turn adversarial attacks into semantically meaningful perturbations, without modifying the classifiers to explain. The proposed approach hypothesizes that Denoising Diffusion Probabilistic Models are excellent regularizers for avoiding high-frequency and out-of-distribution perturbations when generating adversarial attacks. The paper's key idea is to build attacks through a diffusion model to polish them. This allows studying the target model regardless of its robustification level. Extensive experimentation shows the advantages of our counterfactual explanation approach over current State-of-the-Art in multiple testbeds.

关键词： Explainable computer vision

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ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image

ANIM: Accurate Neural Implicit Model for Human Reconstructio...

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

作者： Pesavento, Marco Xu, Yuanlu Sarafianos, Nikolaos Maier, Robert Wang, Ziyan Yao, Chun-Han Volino, Marco Boyer, Edmond Hilton, Adrian Tung, Tony Univ Surrey CVSSP Guildford Surrey England UC Merced Merced CA USA Meta Real Labs Sausalito CA USA

ISBN: (纸本)9798350353013;9798350353006

Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB image. However, existing monocular approaches still struggle to recover fine geometric details such as face, hands or cloth wrinkles. They are also easily prone to depth ambiguities that result in distorted geometries along the camera optical axis. In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy. Our model learns geometric details from both multi-resolution pixel-aligned and voxel-aligned features to leverage depth information and enable spatial relationships, mitigating depth ambiguities. We further enhance the quality of the reconstructed shape by introducing a depth-supervision strategy, which improves the accuracy of the signed distance field estimation of points that lie on the re-constructed surface. Experiments demonstrate that ANIM outperforms state-of-the-art works that use RGB, surface normals, point cloud or RGB-D data as input. In addi-tion, we introduce ANIM-Real, a new multi-modal dataset comprising high-quality scans paired with consumer-grade RGB-D camera, and our protocol to fine-tune ANIM, enabling high-quality reconstruction from real-world human capture. https://***/ANIM/

关键词： 3D Digital Avatars 3D Human reconstruction computer vision Neural Implicit Model

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Video Representation Learning for Conversational Facial Expression recognition Guided by Multiple View Reconstruction

Video Representation Learning for Conversational Facial Expr...

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

作者： Strizhkova, Valeriya Ferrari, Laura M. Kachmar, Hadi Dantcheva, Antitza Bremond, Francois INRIA Paris France Univ Cote dAzur Nice France

ISBN: (纸本)9798350365474

Conversational facial expression recognition entails challenges such as handling of facial dynamics, small available datasets, low-intensity and fine-grained emotional expressions and extreme face angle. Towards addressing these challenges, we propose the Masking Action Units and Reconstructing multiple Angles (MAURA) pre-training. MAURA is an efficient self-supervised method that permits the use of small datasets, while preserving end-toend conversational facial expression recognition with vision Transformer. MAURA masks videos using the location with active Action Units and reconstructs synchronized multi-view videos, thus learning the dependencies between muscle movements and encoding information, which might only be visible in few frames and/or in certain views. Based on one view (e.g., frontal), the encoder reconstructs other views (e.g., top, down, laterals). Such masking and reconstructing strategy provides a powerful representation, beneficial in facial expression downstream tasks. Our experimental analysis shows that we consistently outperform the state-of-the-art in the challenging settings of low-intensity and fine-grained conversational facial expression recognition on four datasets including in-the-wild DFEW, CMU-MOSEI, MFA and multi-view MEAD. Our results suggest that MAURA is able to learn robust and generic video representations.

关键词： Signal encoding

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