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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

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Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation

Instance-aware Exploration-Verification-Exploitation for Ins...

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

作者： Lei, Xiaohan Wang, Min Zhou, Wengang Li, Li Li, Houqiang Univ Sci & Technol China MoE Key Lab Brain Inspired Intelligent Percept & Hefei Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Anhui Peoples R China

ISBN: (纸本)9798350353006

As a new embodied vision task, Instance ImageGoal Navigation (IIN) aims to navigate to a specified object depicted by a goal image in an unexplored environment. The main challenge of this task lies in identifying the target object from different viewpoints while rejecting similar distractors. Existing ImageGoal Navigation methods usually adopt the simple Exploration-Exploitation framework and ignore the identification of specific instance during navigation. In this work, we propose to imitate the human behaviour of "getting closer to confirm" when distinguishing objects from a distance. Specifically, we design a new modular navigation framework named Instance-aware Exploration-Verification- Exploitation (IEVE) for instance-level image goal navigation. Our method allows for active switching among the exploration, verification, and exploitation actions, thereby facilitating the agent in making reasonable decisions under different situations. On the challenging HabitatMatterport 3D semantic (HM3D-SEM) dataset, our method surpasses previous state-of-the-art work, with a classical segmentation model (0.684 vs. 0.561 success) or a robust model (0.702 vs. 0.561 success). Our code will be made publicly available at https://***/XiaohanLei/IEVE.

关键词： Embodied vision Verification Visual Navigation

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PIGEON: Predicting Image Geolocations

PIGEON: Predicting Image Geolocations

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

作者： Haas, Lukas Skreta, Michal Alberti, Silas Finn, Chelsea Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350353006

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy, success in prior literature is constrained to narrow distributions of images of landmarks, and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation, multi-task contrastive pretraining, and a novel loss function. Additionally, our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization;the first model, PIGEON, is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans, ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers, winning all six games. Our second model, PIGEOTTO, differs in that it is trained on a dataset of images from Flickr and Wikipedia, achieving state-of-the-art results on a wide range of image geolocalization benchmarks, outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate, planet-scale image geolocalization systems. Our code is available on GitHub.(1)

关键词： Blind Experiment CLIP Clustering computer vision GeoGuessr Haversine Image Geolocalization Location Refinement Multi-Modal Multi-Task Multi-Task Pretraining Photo Geolocalization Pretraining Retrieval Semantic Geocells Visual Place recognition Voronoi

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RMT: Retentive Networks Meet vision Transformers

RMT: Retentive Networks Meet Vision Transformers

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

作者： Fan, Qihang Huang, Huaibo Chen, Mingrui Liu, Hongmin He, Ran Chinese Acad Sci Inst Automat MAIS & CRIPAC Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

vision Transformer (ViT) has gained increasing attention in the computer vision community in recent years. However, the core component of ViT, Self-Attention, lacks explicit spatial priors and bears a quadratic computational complexity, thereby constraining the applicability of ViT. To alleviate these issues, we draw inspiration from the recent Retentive Network (RetNet) in the field of NLP, and propose RMT, a strong vision backbone with explicit spatial prior for general purposes. Specifically, we extend the RetNet's temporal decay mechanism to the spatial domain, and propose a spatial decay matrix based on the Manhattan distance to introduce the explicit spatial prior to Self-Attention. Additionally, an attention decomposition form that adeptly adapts to explicit spatial prior is proposed, aiming to reduce the computational burden of modeling global information without disrupting the spatial decay matrix. Based on the spatial decay matrix and the attention decomposition form, we can flexibly integrate explicit spatial prior into the vision backbone with linear complexity. Extensive experiments demonstrate that RMT exhibits exceptional performance across various vision tasks. Specifically, without extra training data, RMT achieves 84.8% and 86.1% top-1 acc on ImageNet-1k with 27M/4.5GFLOPs and 96M/18.2GFLOPs. For downstream tasks, RMT achieves 54.5 box AP and 47.2 mask AP on the COCO detection task, and 52.8 mIoU on the ADE20K se-mantic segmentation task.

关键词： vision Transformer

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Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification

Leveraging Cross-Modal Neighbor Representation for Improved ...

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

作者： Yi, Chao Ren, Lu Zhan, De-Chuan Ye, Han-Jia Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However, without specific optimization for unimodal scenarios, its performance in single-modality feature extraction might be suboptimal. Despite this, some studies have directly used CLIP's image encoder for tasks like few-shot classification, introducing a misalignment between its pre-training objectives and feature extraction methods. This inconsistency can diminish the quality of the image's feature representation, adversely affecting CLIP's effectiveness in target tasks. In this paper, we view text features as precise neighbors of image features in CLIP's space and present a novel CrOss-moDal nEighbor Representation (CODER) based on the distance structure between images and their neighbor texts. This feature extraction method aligns better with CLIP's pre-training objectives, thereby fully leveraging CLIP's robust cross-modal capabilities. The key to construct a high-quality CODER lies in how to create a vast amount of high-quality and diverse texts to match with images. We introduce the Auto Text Generator (ATG) to automatically generate the required texts in a data-free and training-free manner. We apply CODER to CLIP's zero-shot and few-shot image classification tasks. Experiment results across various datasets and models confirm CODER's effectiveness. Code is available at: https://***/YCaigogogo/cvpr24-CODER.

关键词： Multimodal Learning vision-Language Model

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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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Visual Program Distillation: Distilling Tools and Programmatic Reasoning into vision-Language Models

Visual Program Distillation: Distilling Tools and Programmat...

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

作者： Hu, Yushi Stretcu, Otilia Lu, Chun-Ta Viswanathan, Krishnamurthy Hata, Kenji Luo, Enming Krishna, Ranjay Fuxman, Ariel Google Res Mountain View CA 94043 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350353006

Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space, recognizing instruments, and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an executable program that invokes specialized vision models. However, generated programs are error-prone: they omit necessary steps, include spurious ones, and are unable to recover when the specialized models give incorrect outputs. Moreover, they require loading multiple models, incurring high latency and computation costs. We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass. VPD distills the reasoning ability of LLMs by using them to sample multiple candidate programs, which are then executed and verified to identify a correct one. It translates each correct program into a language description of the reasoning steps, which are then distilled into a VLM. Extensive experiments show that VPD improves the VLM's ability to count, understand spatial relations, and reason compositionally. Our VPD-trained PaLI-X outperforms all prior VLMs, achieving state-of-the-art performance across complex vision tasks, including MMBench, OK-VQA, A-OKVQA, TallyQA, POPE, and Hateful Memes. An evaluation with human annotators also confirms that VPD improves model response factuality and consistency. Finally, experiments on content moderation demonstrate that VPD is also helpful for adaptation to real-world applications with limited data.

关键词： cross-modal reasoning distillation instruction tuning large language model multimodal synthetic data tools vision-language model visual programming

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From Coarse to Fine-Grained Open-Set recognition

From Coarse to Fine-Grained Open-Set Recognition

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

作者： Lang, Nico Snaebjarnarson, Vesteinn Cole, Elijah Mac Aodha, Oisin Igel, Christian Belongie, Serge Univ Copenhagen Copenhagen Denmark Altos Labs San Diego CA USA Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9798350353006

Open-set recognition (OSR) methods aim to identify whether or not a test example belongs to a category observed during training. Depending on how visually similar a test example is to the training categories, the OSR task can be easy or extremely challenging. However, the vast majority of previous work has studied OSR in the presence of large, coarse-grained semantic shifts. In contrast, many real-world problems are inherently fine-grained, which means that test examples may be highly visually similar to the training categories. Motivated by this observation, we investigate three aspects of OSR: label granularity, similarity between the open- and closed-sets, and the role of hierarchical supervision during training. To study these dimensions, we curate new open-set splits of a large fine-grained visual categorization dataset. Our analysis results in several interesting findings, including: (i) the best OSR method to use is heavily dependent on the degree of semantic shift present, and (ii) hierarchical representation learning can improve coarse-grained OSR, but has little effect on fine-grained OSR performance. To further enhance fine-grained OSR performance, we propose a hierarchy-adversarial learning method to discourage hierarchical structure in the representation space, which results in a perhaps counter-intuitive behaviour, and a relative improvement in fine-grained OSR of up to 2% in AUROC and 7% in AUPR over standard training. Code and data are available: ***/fine-grained-osr.

关键词： anomaly detection classification fine-grained iNaturalist novelty detection OOD Open-set recognition OSR Out-of-distribution detection

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Scene Adaptive Sparse Transformer for Event-based Object Detection

Scene Adaptive Sparse Transformer for Event-based Object Det...

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

作者： Peng, Yansong Li, Hebei Zhang, Yueyi Sun, Xiaoyan Wu, Feng Univ Sci & Technol China Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350353006

While recent Transformer-based approaches have shown impressive performances on event-based object detection tasks, their high computational costs still diminish the low power consumption advantage of event cameras. Image-based works attempt to reduce these costs by introducing sparse Transformers. However, they display inade-quate sparsity and adaptability when applied to event-based object detection, since these approaches cannot balance the fine granularity of token-level sparsification and the efficiency of window-based Transformers, leading to reduced performance and efficiency. Furthermore, they lack scene-specific sparsity optimization, resulting in information loss and a lower recall rate. To overcome these limitations, we propose the Scene Adaptive Sparse Transformer ( SAST). SAST enables window-token co-sparsification, significantly enhancing fault tolerance and reducing computational overhead. Leveraging the innovative scoring and selection modules, along with the Masked Sparse Window Self-Attention, SAST showcases remarkable scene-aware adaptability: It focuses only on important objects and dynamically optimizes sparsity level according to scene complexity, maintaining a remarkable balance between performance and computational cost. The evaluation results show that SAST outperforms all other dense and sparse networks in both performance and efficiency on two large-scale event-based object detection datasets (1Mpx and Gen1). Code: https://***/Peterande/SAST.

关键词： Object recognition

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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...

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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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A Sliced Wasserstein Loss for Neural Texture Synthesis

A Sliced Wasserstein Loss for Neural Texture Synthesis

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

作者： Heitz, Eric Vanhoey, Kenneth Chambon, Thomas Belcour, Laurent Unity Technol Grenoble France

ISBN: (纸本)9781665445092

We address the problem of computing a textural loss based on the statistics extracted from the feature activations of a convolutional neural network optimized for object recognition (e.g. VGG-19). The underlying mathematical problem is the measure of the distance between two distributions in feature space. The Gram-matrix loss is the ubiquitous approximation for this problem but it is subject to several shortcomings. Our goal is to promote the Sliced Wasserstein Distance as a replacement for it. It is theoretically proven, practical, simple to implement, and achieves results that are visually superior for texture synthesis by optimization or training generative neural networks.

关键词： Training Neural networks computer architecture Production Tools Feature extraction Complexity theory

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