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Dual-View Visual Contextualization for Web Navigation

Dual-View Visual Contextualization for Web Navigation

引用

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

来源：评论

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Extending global-local view alignment for self-supervised learning with remote sensing imagery

Extending global-local view alignment for self-supervised le...

引用

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

作者： Wanyan, Xinye Seneviratne, Sachith Shen, Shuchang Kirley, Michael

ISBN: (纸本)9798350365474

Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudolabels for massive unlabeled data to provide supervision for training. While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art results on natural imagery. Inspired by DINO [6], which employs an effective representation learning structure with knowledge distillation based on global-local view alignment, we formulate two pretext tasks for self-supervised learning on remote sensing imagery (SSLRS). Using these tasks, we explore the effectiveness of positive temporal contrast as well as multi-sized views on SSLRS. We extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size in order to alleviate the limited variation in object size observed in remote sensing imagery. Our experiments demonstrate that even when pre-trained on only 10% of the dataset, DINO-MC performs on par or better than existing state-of-the-art SSLRS methods on multiple remote sensing tasks, while using less computational resources. All codes, models, and results are released at https://***/WennyXY/DINO-MC.

关键词： computer vision remote sensing imagery self-supervised learning

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Learning from One Continuous Video Stream

Learning from One Continuous Video Stream

引用

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

作者： Carreira, Joao King, Michael Patraucean, Viorica Gokal, Dilara Ionescu, Cristian Yang, Yi Zoran, Daniel Heyward, Joseph Doersch, Carl Aytar, Yusuf Damen, Di Liu Zisserman, Andrew Google DeepMind London 1 England Univ Bristol Bristol Avon England Univ Oxford Oxford England

ISBN: (纸本)9798350353006

We introduce a framework for online learning from a single continuous video stream - the way people and animals learn, without mini-batches, data augmentation or shuffling. This poses great challenges given the high correlation between consecutive video frames and there is very little prior work on it. Our framework allows us to do a first deep dive into the topic and includes a collection of streams and tasks composed from two existing video datasets, plus methodology for performance evaluation that considers both adaptation and generalization. We employ pixel-to-pixel modelling as a practical and flexible way to switch between pre-training and single-stream evaluation as well as between arbitrary tasks, without ever requiring changes to models and always using the same pixel loss. Equipped with this framework we obtained large single-stream learning gains from pre-training with a novel family of future prediction tasks, found that momentum hurts, and that the pace of weight updates matters. The combination of these insights leads to matching the performance of IID learning with batch size 1, when using the same architecture and without costly replay buffers. An overview of the paper is available online at https://***/view/one-stream-video.

关键词： computer vision continual learning developmental learning

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Improving Image Restoration through Removing Degradations in Textual Representations

Improving Image Restoration through Removing Degradations in...

引用

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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Vim4Path: Self-Supervised vision Mamba for Histopathology Images

Vim4Path: Self-Supervised Vision Mamba for Histopathology Im...

引用

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

作者： Nasiri-Sarvi, Ali Vincent Quoc-Huy Trinh Rivaz, Hassan Hosseini, Mahdi S. Concordia Univ Dept Comp Sci & Software Engn CSSE Montreal PQ Canada Univ Montreal Inst Res Immunol & Canc Montreal PQ Canada Concordia Univ Dept Elect & Comp Engn ECE Montreal PQ Canada

ISBN: (纸本)9798350365474

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim's enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim's capabilities, which reveals that Vim uniquely emulates the pathologist workflow-unlike ViT. This alignment with human expert analysis highlights Vim's potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at https: //***/AtlasAnalyticsLab/Vim4Path.

关键词： Cancer Diagnosis Computational Pathology Self-Supervised Learning vision Mamba Whole Slide Image

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One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained vision-Language Models

One Prompt Word is Enough to Boost Adversarial Robustness fo...

引用

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

作者： Lin, L. Guan, Haoyan Qiu, Jianing Spratling, Michael Kings Coll London London England Imperial Coll London London England

ISBN: (纸本)9798350353006

Large pre-trained vision-Language Models (VLMs) like CLIP, despite having remarkable generalization ability, are highly vulnerable to adversarial examples. This work studies the adversarial robustness of VLMs from the novel perspective of the text prompt instead of the extensively studied model weights (frozen in this work). We first show that the effectiveness of both adversarial attack and defense are sensitive to the used text prompt. Inspired by this, we propose a method to improve resilience to adversarial attacks by learning a robust text prompt for VLMs. The proposed method, named Adversarial Prompt Tuning (APT), is effective while being both computationally and data efficient. Extensive experiments are conducted across 15 datasets and 4 data sparsity schemes (from 1-shot to full training data settings) to show APT's superiority over hand-engineered prompts and other state-of-the-art adaption methods. APT demonstrated excellent abilities in terms of the in-distribution performance and the generalization under input distribution shift and across datasets. Surprisingly, by simply adding one learned word to the prompts, APT can significantly boost the accuracy and robustness ((sic)=4/255 ) over the hand-engineered prompts by +13% and +8.5% on average respectively. The improvement further increases, in our most effective setting, to +26.4% for accuracy and +16.7% for robustness. Code is available at https://***/TreeLLi/APT.

关键词： adversarial examples adversarial robustness CLIP text prompting vision-language models VLMs

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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

Masked Autoencoders for Microscopy are Scalable Learners of ...

引用

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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Material Palette: Extraction of Materials from a Single Image

Material Palette: Extraction of Materials from a Single Imag...

引用

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

作者： Lopes, Ivan Pizzati, Fabio de Charette, Raoul INRIA Paris France Univ Oxford Oxford England

ISBN: (纸本)9798350353013;9798350353006

Physically-Based Rendering (PBR) is key to modeling the interaction between light and materials, and finds extensive applications across computer graphics domains. However, acquiring PBR materials is costly and requires special apparatus. In this paper, we propose a method to extract PBR materials from a single real-world image. We do so in two steps: first, we map regions of the image to material concept tokens using a diffusion model, allowing the sampling of texture images resembling each material in the scene. Second, we leverage a separate network to decompose the generated textures into spatially varying BRDFs (SVBRDFs), offering us readily usable materials for rendering applications. Our approach relies on existing synthetic material libraries with SVBRDF ground truth. It exploits a diffusion-generated RGB texture dataset to allow generalization to new samples using unsupervised domain adaptation (UDA). Our contributions are thoroughly evaluated on synthetic and real-world datasets. We further demonstrate the applicability of our method for editing 3D scenes with materials estimated from real photographs. Along with video, we share code and models as open-source on the project page: https://***/astra-vision/MaterialPalette

关键词： brdf computer graphics generative ai inversion Material estimation

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Gated Fields: Learning Scene Reconstruction from Gated Videos

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

引用

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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Pre-trained vision and Language Transformers Are Few-Shot Incremental Learners

Pre-trained Vision and Language Transformers Are Few-Shot In...

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

作者： Park, Keon-Hee Song, Kyungwoo Park, Gyeong-Moon Kyung Hee Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting, and these challenges have driven prior studies to primarily rely on shallow models, such as ResNet-18. Even though their limited capacity can mitigate both forgetting and overfitting issues, it leads to inadequate knowledge transfer during few-shot incremental sessions. In this paper, we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners. To this end, we propose a novel FSCIL framework called PriViLege, Pre-trained vision and Language transformers with prompting functions and knowledge distillation. Our framework effectively addresses the challenges of catastrophic forgetting and overfitting in large models through new pre-trained knowledge tuning (PKT) and two losses: entropy-based divergence loss and semantic knowledge distillation loss. Experimental results show that the proposed PriViLege significantly outperforms the existing state-of-the-art methods with a large margin, e.g., +9.38% in CUB200, +20.58% in CIFAR-100, and +13.36% in miniImageNet. Our implementation code is available at https://***/KHU-AGI/PriViLege.

关键词： Few-shot learning Incremental learning Parameter Efficient Tuning

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