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共 20950 条记录，以下是511-520 订阅

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RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback

RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment fro...

引用

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

作者： Yu, Tianyu Yao, Yuan Zhang, Haoye He, Taiwen Han, Yifeng Cui, Ganqu Hu, Jinyi Liu, Zhiyuan Zheng, Hai-Tao Sun, Maosong Tsinghua Univ Beijing Peoples R China Natl Univ Singapore Singapore Singapore Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction. However, existing MLLMs prevalently suffer from serious hallucination problems, generating text that is not factually grounded in associated images. The problem makes existing MLLMs untrustworthy and thus impractical in real-world (especially high-stakes) applications. To address the challenge, we present RLHF-V, which enhances MLLM trustworthiness via behavior alignment from fine-grained correctional human feedback. Specifically, RLHF-V collects human preference in the form of segment-level corrections on hallucinations, and performs dense direct preference optimization over the human feedback. Comprehensive experiments on five benchmarks in both automatic and human evaluation show that, RLHF-V can enable substantially more trustworthy MLLM behaviors with promising data and computation efficiency. Remarkably, using 1.4k annotated data samples, RLHF-V significantly reduces the hallucination rate of the base MLLM by 34.8%, outperforming the concurrent LLaVA-RLHF trained on 10k annotated data. The final model achieves state-of-the-art performance in trustworthiness among open-source MLLMs, and shows better ro-bustness than GPT-4V in preventing hallucinations aroused from over-generalization.

关键词： hallucination language reasoning vision

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Theoretically Achieving Continuous Representation of Oriented Bounding Boxes

Theoretically Achieving Continuous Representation of Oriente...

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

作者： Xiao, Zi-Kai Yang, Guo-Ye Yang, Xue Mul, Tai-Jiang Yan, Junchi Hui, Shi-Min Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab AI Shanghai Peoples R China

ISBN: (纸本)9798350353006

Considerable efforts have been devoted to Oriented Object Detection (OOD). However, one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) representation remains unresolved, which is an inherent bottleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to the ad-hoc efforts in this direction. Prior studies typically can only address one of the two cases of discontinuity: rotation and aspect ratio, and often inadvertently introduce decoding discontinuity, e.g. Decoding Incompleteness (DI) and Decoding Ambiguity (DA) as discussed in literature. Specifically, we propose a novel representation method called Continuous OBB (COBB), which can be readily integrated into existing detectors e.g. Faster-RCNN as a plugin. It can theoretically ensure continuity in bounding box regression which to our best knowledge, has not been achieved in literature for rectangle-based object representation. For fairness and transparency of experiments, we have developed a modularized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDet for OOD evaluation. On the popular DOTA dataset, by integrating Faster-RCNN as the same baseline model, our new method outperforms the peer method Gliding Vertex by 1.13% mAP(50) (relative improvement 1.54%), and 2.46% mAP(75) (relative improvement 5.91%), without any tricks.

关键词： boundary problem computer vision object detection oriented object detection remote sensing

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

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

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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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D⁴M: Dataset Distillation via Disentangled Diffusion Model

D<SUP>4</SUP>M: Dataset Distillation via Disentangled Diffus...

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

作者： Su, Duo Hou, Junjie Gao, Weizhi Tian, Yingjie Tang, Bowen UCAS Sch Comp Sci & Technol Beijing Peoples R China UCAS Sino Danish Coll Beijing Peoples R China NCSU Dept Comp Sci Raleigh NC USA UCAS Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Polic Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designan economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce Dataset Distillation via Disentangled Diffusion Model ((DM)-M-4), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D4(M) employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, (DM)-M-4 demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects.

关键词： computer vision Data-Centric AI Dataset Distillation Latent Diffusion Model Prototype Learning

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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...

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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...

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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

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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...

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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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Generating Diverse Agricultural Data for vision-Based Farming Applications

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

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

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

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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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