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共 21179 条记录，以下是281-290 订阅

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DRESS: Instructing Large vision-Language Models to Align and Interact with Humans via Natural Language Feedback

DRESS: Instructing Large Vision-Language Models to Align and...

引用

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

作者： Chen, Yangyi Sikka, Karan Cogswell, Michael Ji, Heng Divakaran, Ajay SRI Int Menlo Pk CA 94025 USA Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9798350353006

We present DRESS, a large vision language model (LVLM) that innovatively exploits Natural Language feedback (NLF) from Large Language Models to enhance its alignment and interactions by addressing two key limitations in the state-of-the-art LVLMs. First, prior LVLMs generally rely only on the instruction finetuning stage to enhance alignment with human preferences. Without incorporating extra feedback, they are still prone to generate unhelpful, hallucinated, or harmful responses. Second, while the visual instruction tuning data is generally structured in a multi-turn dialogue format, the connections and dependencies among consecutive conversational turns are weak. This reduces the capacity for effective multi-turn interactions. To tackle these, we propose a novel categorization of the NLF into two key types: critique and refinement. The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences. The refinement NLF offers concrete suggestions for improvement and is adopted to improve the interaction ability of the LVLMs- which focuses on LVLMs' ability to refine responses by incorporating feedback in multi-turn interactions. To address the non-differentiable nature of NLF, we generalize conditional reinforcement learning for training. Our experimental results demonstrate that DRESS can generate more helpful (9.76%), honest (11.52%), and harmless (21.03%) responses, and more effectively learn from feedback during multi-turn interactions compared to SOTA LVLMs.

关键词： Alignment Interaction Large vision Language Models Natural Language Feedback

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Dual-View Visual Contextualization for Web Navigation

Dual-View Visual Contextualization for Web Navigation

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

作者： Kil, Jihyung Song, Chan Hee Zheng, Boyuan Deng, Xiang Su, Yu Chao, We-Lun Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9798350353006

Automatic web navigation aims to build a web agent that can follow language instructions to execute complex and diverse tasks on real-world websites. Existing work primarily takes HTML documents as input, which define the contents and action spaces (i.e., actionable elements and operations) of webpages. Nevertheless, HTML documents may not provide a clear task-related context for each element, making it hard to select the right (sequence of) actions. In this paper, we propose to contextualize HTML elements through their "dual views" in webpage screenshots: each HTML element has its corresponding bounding box and visual content in the screenshot. We build upon the insight-web developers tend to arrange task-related elements nearby on webpages to enhance user experiences-and propose to contextualize each element with its neighbor elements, using both textual and visual features. The resulting representations of HTML elements are more informative for the agent to take action. We validate our method on the recently released Mind2Web dataset, which features diverse navigation domains and tasks on real-world websites. Our method consistently outperforms the baseline in all the scenarios, including cross-task, cross-website, and cross-domain ones.

关键词： AI Agents Multimodal Agents Multimodal Learning vision-Language Web Agents Web Navigation

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Gated Fields: Learning Scene Reconstruction from Gated Videos

Gated Fields: Learning Scene Reconstruction from Gated Video...

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

作者： Ramazzina, Andrea Walz, Stefanie Dahal, Pragyan Bijelic, Mario Heide, Felix Mercedes Benz Stuttgart Germany Saarland Univ Saarbrucken Germany Politecn Milan Milan Italy Torc Robot Blacksburg VA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However, existing methods that recover scene properties, such as geometry, appearance, or radiance, solely from RGB captures often fail when handling poorly-lit or texture-deficient regions. Similarly, recovering scenes with scanning LiDAR sensors is also difficult due to their low angular sampling rate which makes recovering expansive real-world scenes difficult. Tackling these gaps, we introduce Gated Fields - a neural scene reconstruction method that utilizes active gated video sequences. To this end, we propose a neural rendering approach that seamlessly incorporates time-gated capture and illumination. Our method exploits the intrinsic depth cues in the gated videos, achieving precise and dense geometry reconstruction irrespective of ambient illumination conditions. We validate the method across day and night scenarios and find that Gated Fields compares favorably to RGB and LiDAR reconstruction methods. Our code and datasets are available here(1).

关键词： 3D vision Gated Camera Neural Radiance Fields Neural Rendering

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Prompting vision Foundation Models for Pathology Image Analysis

Prompting Vision Foundation Models for Pathology Image Analy...

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

作者： Yin, Chong Liu, Siqi Zhou, Kaiyang Wong, Vincent Wai-Sun Yuen, Pong C. Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Dept Med & Therapeut Hong Kong Peoples R China

ISBN: (纸本)9798350353006

The rapid increase in cases of non-alcoholic fatty liver disease (NAFLD) in recent years has raised significant public concern. Accurately identifying tissue alteration regions is crucial for the diagnosis of NAFLD, but this task presents challenges in pathology image analysis, particularly with small-scale datasets. Recently, the paradigm shift from full fine-tuning to prompting in adapting vision foundation models has offered a new perspective for small-scale data analysis. However, existing prompting methods based on task-agnostic prompts are mainly developed for generic image recognition, which fall short in providing instructive cues for complex pathology images. In this paper, we propose Quantitative Attribute-based Prompting (QAP), a novel prompting method specifically for liver pathology image analysis. QAP is based on two quantitative attributes, namely K-function-based spatial attributes and histogram-based morphological attributes, which are aimed for quantitative assessment of tissue states. Moreover, a conditional prompt generator is designed to turn these instance-specific attributes into visual prompts. Extensive experiments on three diverse tasks demonstrate that our task-specific prompting method achieves better diagnostic performance as well as better interpretability. Code is available at https://***/7LFB/QAP.

关键词： pathology image analysis Prompt quantitative attributes

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Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Mitigating Object Dependencies: Improving Point Cloud Self-S...

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

作者： Wu, Yanhao Zhang, Tong Ke, Wei Qi, Congpei Susstrunk, Sabine Salzmann, Mathieu Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Lausanne Switzerland

ISBN: (纸本)9798350353006

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object cor-relations. This can create a tendency for neural networks to exploit these strong dependencies, bypassing the individual object patterns. To address this challenge, we introduce a novel self-supervised learning (SSL) strategy. Our approach leverages both object patterns and contextual cues to produce robust features. It begins with the formulation of an object-exchanging strategy, where pairs of objects with comparable sizes are exchanged across different scenes, effectively disentangling the strong contextual dependencies. Subsequently, we introduce a context-aware feature learning strategy, which encodes object patterns without relying on their specific context by aggregating object features across various scenes. Our extensive experiments demonstrate the superiority of our method over existing SSL techniques, further showing its better robustness to environmen tal changes. Moreover, we showcase the applicability of our approach by transferring pre-trained models to diverse point cloud datasets.

关键词： 3D vision self-supervised learning

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CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation

CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Inter...

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

作者： Chowdhury, Townim Faisal Liao, Kewen Vu Minh Hieu Phan To, Minh-Son Xie, Yutong Hung, Kevin Rose, David van den Hengel, Anton Verjans, Johan W. Liao, Zhibin Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Australian Catholic Univ Fitzroy Vic Australia Flinders Univ S Australia Adelaide SA Australia Cent Adelaide Local Hlth Network SA Pathol Adelaide SA Australia

ISBN: (纸本)9798350353006

Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways to visually explain the DNN decision-making process by displaying 'attention' heatmaps of the DNNs. Nevertheless, the CAM explanation only offers relative attention information, that is, on an attention heatmap, we can interpret which image region is more or less important than the others. However, these regions cannot be meaningfully compared across classes, and the contribution of each region to the model's class prediction is not revealed. To address these challenges that ultimately lead to better DNN Interpretation, in this paper, we propose CAPE, a novel reformulation of CAM that provides a unified and probabilistically meaningful assessment of the contributions of image regions. We quantitatively and qualitatively compare CAPE with state-of-the-art CAM methods on CUB and ImageNet benchmark datasets to demonstrate enhanced interpretability. We also test on a cytology imaging dataset depicting a challenging Chronic Myelomono-cytic Leukemia (CMML) diagnosis problem. Code is available at: https://***/AIML-MED/CAPE.

关键词： class activation maps deep neural network interpretability explainable AI image recognition visual classification

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MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

MaskCLR: Attention-Guided Contrastive Learning for Robust Ac...

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

作者： Abdelfattah, Mohamed Hassan, Mariam Alahi, Alexandre Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9798350353006

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changing the pose estimator between training and testing. In this work, we introduce MaskCLR, a new Masked Contrastive Learning approach for Robust skeletal action recognition. We propose an Attention-Guided Proba-bilistic Masking strategy to occlude the most important joints and encourage the model to explore a larger set of discrimi-native joints. Furthermore, we propose a Multi-Level Contrastive Learning paradigm to enforce the representations of standard and occluded skeletons to be class-discriminative, i.e., more compact within each class and more dispersed across different classes. Our approach helps the model capture the high-level action semantics instead of low-level joint variations, and can be conveniently incorporated into transformer-based models. Without loss of generality, we combine MaskCLR with three transformer backbones: the vanilla transformer, DSTFormer, and STTFormer. Extensive experiments on NTU60, NTU120, and Kinetics400 show that MaskCLR consistently outperforms previous state-of-the-art methods on standard and perturbed skeletons from different pose estimators, showing improved accuracy, generalization, and robustness. Project website: https://***.

关键词： contrastive learning Skeleton-based action recognition

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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

Masked Autoencoders for Microscopy are Scalable Learners of ...

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

作者： Krauss, Oren Kenyon-Dean, Kian Saberian, Saber Fallah, Maryam McLean, Peter Leung, Jess Sharma, Vasudev Khan, Ayla Balakrishnan, Jia Celik, Safiye Beaini, Dominique Sypetkowski, Maciej Cheng, Chi Vicky Morsel, Kristen Makes, Maureen Mabey, Ben Earnshaw, Berton Recurs Salt Lake City UT 84101 USA Valence Labs Rajpura India

ISBN: (纸本)9798350353006

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally, we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond. Relevant code and select models released with this work can be found at: https://***/recursionpharma/maes_microscopy.

关键词： cell biology cell morphology microscopy self-supervised learning SSL vision transformer ViT

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Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect vision-Language Pre-training Framework

Decomposing Disease Descriptions for Enhanced Pathology Dete...

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

作者： Vu Minh Hieu Phan Xie, Yutong Qi, Yuankai Liu, Linggiao Liu, Liyang Zhang, Bowen Liao, Zhibin Wu, Qi To, Minh-Son Verjans, Johan W. Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Macquarie Univ Sydney NSW Australia Flinders Univ S Australia Adelaide SA Australia

ISBN: (纸本)9798350353006

Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts, current methods struggle to align medical images with key pathological findings in unstructured reports. This leads to the misalignment with the target disease's textual representation. In this paper, we introduce a novel VLP framework designed to dissect disease descriptions into their fundamental aspects, leveraging prior knowledge about the visual manifestations of pathologies. This is achieved by consulting a large language model and medical experts. Integrating a Transformer module, our approach aligns an input image with the diverse elements of a disease, generating aspect-centric image representations. By consolidating the matches from each aspect, we improve the compatibility between an image and its associated disease. Additionally, capitalizing on the aspect-oriented representations, we present a dual-head Transformer tailored to process known and unknown diseases, optimizing the comprehensive detection efficacy. Conducting experiments on seven downstream datasets, ours improves the accuracy of recent methods by up to 8.56% and 17.26% for seen and unseen categories, respectively. Our code is released at https://***/HieuPhan33/MAVL.

关键词： Medical vision-language pre-training vision-language pre-training Visual grounding Zero-shot classification

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Improving Image Restoration through Removing Degradations in Textual Representations

Improving Image Restoration through Removing Degradations in...

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

作者： Lin, Jingbo Zhang, Zhilu Wei, Yuxiang Ren, Dongwei Jiang, Dongsheng Tian, Qi Zuo, Wangmeng Harbin Inst Technol Harbin Peoples R China Huawei Cloud Comp Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively, restoration is much easier on text modality than image one. For example, it can be easily conducted by removing degradation-related words while keeping the content-aware words. Hence, we combine the advantages of images in detail description and ones of text in degradation removal to perform restoration. To address the cross-modal assistance, we propose to map the degraded images into textual representations for removing the degradations, and then convert the restored textual representations into a guidance image for assisting image restoration. In particular, We ingeniously embed an image-to-text mapper and text restoration module into CLIP-equipped text-to-image models to generate the guidance. Then, we adopt a simple coarse-to-fine approach to dynamically inject multi-scale information from guidance to image restoration networks. Extensive experiments are conducted on various image restoration tasks, including deblurring, dehazing, deraining, and denoising, and all-in-one image restoration. The results showcase that our method outperforms state-of-the-art ones across all these tasks. The codes and models are available at https://***/mrluin/TextualDegRemoval.

关键词： image restoration low-level vision

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