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共 21179 条记录，以下是371-380 订阅

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Modeling Collaborator: Enabling Subjective vision Classification With Minimal Human Effort via LLM Tool-Use

Modeling Collaborator: Enabling Subjective Vision Classifica...

引用

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

作者： Toubal, Imad Eddine Avinash, Aditya Alldrin, Neil Gordon Dlabal, Jan Zhou, Wenlei Luo, Enming Stretcu, Otilia Xiong, Hao Lu, Chun-Ta Zhou, Howard Krishna, Ranjay Fuxman, Ariel Duerig, Tom Google Res Mountain View CA 94043 USA Univ Missouri Columbia MO 65211 USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350353006

From content moderation to wildlife conservation, the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally, developing classifiers for such concepts requires substantial manual effort measured in hours, days, or even months to identify and annotate data needed for training. Even with recently proposed Agile Modeling techniques, which enable rapid bootstrapping of image classifiers, users are still required to spend 30 minutes or more of monotonous, repetitive data labeling just to train a single classifier. Drawing on Fiske's Cognitive Miser theory, we propose a new framework that alleviates manual effort by replacing human labeling with natural language interactions, reducing the total effort required to define a concept by an order of magnitude: from labeling 2,000 images to only 100 plus some natural language interactions. Our framework leverages recent advances in foundation models, both large language models and vision-language models, to carve out the concept space through conversation and by automatically labeling training data points. Most importantly, our framework eliminates the need for crowd-sourced annotations. Moreover, our framework ultimately produces lightweight classification models that are deployable in cost-sensitive scenarios. Across 15 subjective concepts and across 2 public image classification datasets, our trained models outperform traditional Agile Modeling as well as state-of-the-art zero-shot classification models like ALIGN, CLIP, CuPL, and large visual question answering models like PaLI-X.

关键词： agile modeling large language model subjective classification tool-use vision language model

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Color Shift Estimation-and-Correction for Image Enhancement

Color Shift Estimation-and-Correction for Image Enhancement

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

作者： Li, Yiyu Xu, Ke Hancke, Gerhard Petrus Lau, Rynson W. H. City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate color tone distortion in underexposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and over-exposed regions display opposite color tone distribution shifts, which may not be easily normalized in joint modeling as they usually do not have "normal-exposed" regions/pixels as reference. In this paper, we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically, we first derive the color feature maps of the bright-ened and darkened versions of the input image via a UNet-based network, followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened ( or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches. Project web-page: https://***/yiyulics/CSEC.

关键词： computer vision Exposure Correction Image Enhancement Low-level vision Low-light Image Enhancement

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3D Human Pose Perception from Egocentric Stereo Videos

3D Human Pose Perception from Egocentric Stereo Videos

引用

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

作者： Akada, Hiroyasu Wang, Jian Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9798350353013;9798350353006

While head-mounted devices are becoming more compact, they provide egocentric views with significant self-occlusions of the device user. Hence, existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work, we propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation, which leverages the scene information and temporal context of egocentric stereo videos. Specifically, we utilize 1) depth features from our 3D scene reconstruction module with uniformly sampled windows of egocentric stereo frames, and 2) human joint queries enhanced by temporal features of the video inputs. Our method is able to accurately estimate human poses even in challenging scenarios, such as crouching and sitting. Furthermore, we introduce two new benchmark datasets, i.e., UnrealEgo2 and UnrealEgo-RW (RealWorld). The proposed datasets offer a much larger number of egocentric stereo views with a wider variety of human motions than the existing datasets, allowing comprehensive evaluation of existing and upcoming methods. Our extensive experiments show that the proposed approach significantly outperforms previous methods. UnrealEgo2, UnrealEgo-RW, and trained models are available on our project page(1) and Benchmark Challenge(2).

关键词： Egocentric 3D human pose estimation First-person view Stereo vision

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Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts

Toward Generalist Anomaly Detection via In-context Residual ...

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

作者： Zhu, Jiawen Pang, Guansong Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (纸本)9798350353006

This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data. Some recent studies have showed that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets, but their methods rely heavily on handcrafted text prompts about defects, making them difficult to generalize to anomalies in other applications, e.g., medical image anomalies or semantic anomalies in natural images. In this work, we propose to train a GAD model with few-shot normal images as sample prompts for AD on diverse datasets on the fly. To this end, we introduce a novel approach that learns an in-context residual learning model for GAD, termed InCTRL. It is trained on an auxiliary dataset to discriminate anomalies from normal samples based on a holistic evaluation of the residuals between query images and few-shot normal sample prompts. Regardless of the datasets, per definition of anomaly, larger residuals are expected for anomalies than normal samples, thereby enabling InCTRL to generalize across different domains without further training. Comprehensive experiments on nine AD datasets are performed to establish a GAD benchmark that encapsulate the detection of industrial defect anomalies, medical anomalies, and semantic anomalies in both one-vs-all and multi-class setting, on which InCTRL is the best performer and significantly outperforms state-of-the-art competing methods. Code is available at https://***/mala-lab/InCTRL.

关键词： Anomaly Detection Few-shot Anomaly Detection Generalist Anomaly Detection vision Language Model

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CLOVA: A Closed-LOop Visual Assistant with Tool Usage and Update

CLOVA: A Closed-LOop Visual Assistant with Tool Usage and Up...

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

作者： Gao, Zhi Du, Yuntao Zhang, Xintong Ma, Xiaojian Han, Wenjuan Zhu, Song-Chun Li, Qing Peking Univ Sch Intelligence Sci & Technol Beijing Peoples R China BIGAI State Key Lab Gen Artificial Intelligence Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350353006

Utilizing large language models (LLMs) to compose off-the-shelf visual tools represents a promising avenue of research for developing robust visual assistants capable of addressing diverse visual tasks. However, these methods often overlook the potential for continual learning, typically by freezing the utilized tools, thus limiting their adaptation to environments requiring new knowledge. To tackle this challenge, we propose CLOVA, a Closed-LOop Visual Assistant, which operates within a framework encompassing inference, reflection, and learning phases. During the inference phase, LLMs generate programs and execute cor responding tools to complete assigned tasks. In the reflection phase, a multimodal global-local reflection scheme analyzes human feedback to determine which tools require updating. Lastly, the learning phase employs three flexible approaches to automatically gather training data and introduces a novel prompt tuning scheme to update the tools, allowing CLOVA to efficiently acquire new knowledge. Experimental findings demonstrate that CLOVA surpasses existing tool-usage methods by 5% in visual question answering and multiple-image reasoning, by 10% in knowledge tagging, and by 20% in image editing. These results underscore the significance of the continual learning capability in general visual assistants.

关键词： Compositional Reasoning Large Language Models Multimodal Learning vision and Language Visual Assistants

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Constrained Layout Generation with Factor Graphs

Constrained Layout Generation with Factor Graphs

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

作者： Dupty, Mohammed Haroon Dong, Yanfei Leng, Sicong Fu, Guoji Goh, Yong Liang Lu, Wei Lee, Wee Sun Natl Univ Singapore Singapore Singapore Paypal San Jose CA USA Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9798350353006

This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process. The design process typically involves specifying a set of spatial constraints that include object attributes like size and inter-object relations such as relative positioning. Existing works, which typically represent objects as single nodes, lack the granularity to accurately model complex interactions between objects. For instance, often only certain parts of an object, like a room's right wall, interact with adjacent objects. To address this gap, we introduce a factor graph based approach with four latent variable nodes for each room, and a factor node for each constraint. The factor nodes represent dependencies among the variables to which they are connected, effectively capturing constraints that are potentially of a higher order. We then develop message-passing on the bipartite graph, forming a factor graph neural network that is trained to produce a floorplan that aligns with the desired requirements. Our approach is simple and generates layouts faithful to the user requirements, demonstrated by a large improvement in IOU scores over existing methods. Additionally, our approach, being inferential and accurate, is well-suited to the practical human-in-the-loop design process where specifications evolve iteratively, offering a practical and powerful tool for AI-guided design.

关键词： AI guided design Factor graph neural networks Floorplan generation Layout generation vision applications

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Dual Memory Networks: A Versatile Adaptation Approach for vision-Language Models

Dual Memory Networks: A Versatile Adaptation Approach for Vi...

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

作者： Zhang, Yabin Zhu, Wenjie Tang, Hui Ma, Zhiyuan Zhou, Kaiyang Zh, Lei HKPolyU Hong Kong Peoples R China OPPO Hong Kong Peoples R China HKUST Hong Kong Peoples R China HKBU Hong Kong Peoples R China

ISBN: (纸本)9798350353006

With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent research. The adaptation strategies can be typically categorized into three paradigms: zero-shot adaptation, few-shot adaptation, and the recently-proposed training-free few-shot adaptation. Most existing approaches are tailored for a specific setting and can only cater to one or two of these paradigms. In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings. Specifically, we propose the dual memory networks that comprise dynamic and static memory components. The static memory caches training data knowledge, enabling training-free few-shot adaptation, while the dynamic memory preserves historical test features online during the testing process, allowing for the exploration of additional data insights beyond the training set. This novel capability enhances model performance in the few-shot setting and enables model usability in the absence of training data. The two memory networks employ the same flexible memory interactive strategy, which can operate in a training-free mode and can be further enhanced by incorporating learnable projection layers. Our approach is tested across 11 datasets under the three task settings. Remarkably, in the zero-shot scenario, it outperforms existing methods by over 3% and even shows superior results against methods utilizing external training data. Additionally, our method exhibits robust performance against natural distribution shifts. Codes are available at https://***/YBZh/DMN.

关键词： dual memory networks versatile adaptation vision-language models

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CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place recognition

CricaVPR: Cross-image Correlation-aware Representation Learn...

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

作者： Lu, Feng Lan, Xiangyuan Zhang, Lijun Jiang, Dongmei Wang, Yaowei Yuan, Chun Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350353006

Over the past decade, most methods in visual place recognition (VPR) have used neural networks to produce feature representations. These networks typically produce a global representation of a place image using only this image itself and neglect the cross-image variations (e.g. viewpoint and illumination), which limits their robustness in challenging scenes. In this paper, we propose a robust global representation method with cross-image correlation awareness for VPR, named CricaVPR. Our method uses the attention mechanism to correlate multiple images within a batch. These images can be taken in the same place with different conditions or viewpoints, or even captured from different places. Therefore, our method can utilize the cross-image variations as a cue to guide the representation learning, which ensures more robust features are produced. To further facilitate the robustness, we propose a multi-scale convolution-enhanced adaptation method to adapt pre-trained visual foundation models to the VPR task, which introduces the multi-scale local information to further enhance the cross-image correlation-aware representation. Experimental results show that our method outperforms state-of-the-art methods by a large margin with significantly less training time. The code is released at https://***/Lu-Feng/CricaVPR.

关键词： convolution adapter cross-image correlation visual geo-localization visual place recognition

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HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large vision-Language Models

HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled L...

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

作者： Guan, Tianrui Liu, Fuxiao Wu, Xiyang Xian, Ruiqi Li, Zongxia Liu, Xiaoyu Wang, Xijun Chen, Lichang Huang, Furong Yacoob, Yaser Manocha, Dinesh Zhou, Tianyi Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350353006

We introduce "HALLUSIONBENCH(1)," a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs), such as GPT-4V(ision), Gemini Pro vision, Claude 3, and LLaVA-1.5, by emphasizing nuanced understanding and interpretation of visual data. The benchmark comprises 346 images paired with 1129 questions, all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies, logical consistency, and various failure modes. In our evaluation on HALLUSIONBENCH, we benchmarked 15 different models, highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably, all other evaluated models achieve accuracy below 16%. Moreover, our analysis not only highlights the observed failure modes, including language hallucination and visual illusion but also deepens an understanding of these pitfalls. Our comprehensive case studies within HALLUSIONBENCH shed light on the challenges of hallucination and illusion in LVLMs. Based on these insights, we suggest potential pathways for their future improvement. The benchmark and codebase can be accessed at https://***/tianyi-lab/HallusionBench.

关键词： Evaluation Dataset Hallucination vision language model VLM Evaluation

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SHViT: Single-Head vision Transformer with Memory Efficient Macro Design

SHViT: Single-Head Vision Transformer with Memory Efficient ...

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

作者： Yun, Seokju Ro, Youngmin Univ Seoul Machine Intelligence Lab Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Recently, efficient vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally, they use 4x4 patch embeddings and a 4-stage structure at the macro level, while utilizing sophisticated attention with multi-head configuration at the micro level. This paper aims to address computational redundancy at all design levels in a memory-efficient manner. We discover that using larger-stride patchify stem not only reduces memory access costs but also achieves competitive performance by leveraging token representations with reduced spatial redundancy from the early stages. Furthermore, our preliminary analyses suggest that attention layers in the early stages can be substituted with convolutions, and several attention heads in the latter stages are computationally redundant. To handle this, we introduce a single-head attention module that inherently prevents head redundancy and simultaneously boosts accuracy by parallelly combining global and local information. Building upon our solutions, we introduce SHViT, a SingleHead vision Transformer that obtains the state-of-the-art speed-accuracy tradeoff. For example, on ImageNet-1k, our SHViT-S4 is 3.3x, 8.1x, and 2.4x faster than MobileViTv2 x1.0 on GPU, CPU, and iPhone12 mobile device, respectively, while being 1.3% more accurate. For object detection and instance segmentation on MS COCO using MaskRCNN head, our model achieves performance comparable to FastViT-SA12 while exhibiting 3.8x and 2.0x lower backbone latency on GPU and mobile device, respectively.

关键词： CNNs efficient visual backbone vision Transformer

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