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共 23241 条记录，以下是141-150 订阅

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Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box vision-Language Models for Selective Visual Question Answering

Consistency and Uncertainty: Identifying Unreliable Response...

引用

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

作者： Khan, Zaid Fu, Yun Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

The goal of selective prediction is to allow an a model to abstain when it may not be able to deliver a reliable prediction, which is important in safety-critical contexts. Existing approaches to selective prediction typically require access to the internals of a model, require retraining a model or study only unimodal models. However, the most powerful models (e.g. GPT-4) are typically only available as black boxes with inaccessible internals, are not retrainable by end-users, and are frequently used for multimodal tasks. We study the possibility of selective prediction for vision-language models in a realistic, black-box setting. We propose using the principle of neighborhood consistency to identify unreliable responses from a black-box vision-language model in question answering tasks. We hypothesize that given only a visual question and model response, the consistency of the model's responses over the neighborhood of a visual question will indicate reliability. It is impossible to directly sample neighbors in feature space in a black-box setting. Instead, we show that it is possible to use a smaller proxy model to approximately sample from the neighborhood. We find that neighborhood consistency can be used to identify model responses to visual questions that are likely unreliable, even in adversarial settings or settings that are out-of-distribution to the proxy model.

关键词： predictive uncertainty selective prediction trustworthy ml vision-language visual question answering

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Evaluating Confidence Calibration in Endoscopic Diagnosis Models

Evaluating Confidence Calibration in Endoscopic Diagnosis Mo...

引用

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

作者： Dehghani, Nikoo Thijssen, Ayla van der Zander, Quirine E. W. Schreuder, Ramon-Michel Schoon, Erik J. van der Sommen, Fons de With, Peter H. N. Eindhoven Univ Technol Eindhoven Netherlands Maastricht Univ Med Ctr Maastricht Netherlands GROW Res Inst Oncol & Reprod Maastricht Netherlands Catharina Hosp Eindhoven Netherlands Eindhoven Artificial Intelligence Syst Inst Eindhoven Netherlands

ISBN: (纸本)9798350365474

Colorectal polyps are prevalent precursors to colorectal cancer, making their accurate characterization essential for timely intervention and patient outcomes. Deep learning-based computer-aided diagnosis (CADx) systems have shown promising performance in the automated detection and categorization of colorectal polyps (CRP) using endoscopic images. However, alongside the advancement in diagnostic accuracy, the need for reliable and accurate quantification of uncertainty estimates within these systems has become increasingly important. The primary focus of this study is on refining the reliability of computer-aided diagnosis of CRPs within clinical practice. We perform an investigation of widely used model calibration techniques and how they translate into clinical applications, specifically for CRP categorization data. The experiments reveal that the Variational Inference method excels in intra-dataset calibration, but lacks efficiency and inter-dataset generalization. Laplace approximation and temperature scaling methods offer improved calibration across datasets.

关键词： Bayesian neural networks computer-aided diagnosis Confidence calibration Model reliability

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Projecting Trackable Thermal patterns for Dynamic computer vision

Projecting Trackable Thermal Patterns for Dynamic Computer V...

引用

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

作者： Sheinin, Mark Sankaranarayanan, Aswin C. Narasimhan, Srinivasa G. Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

Adding artificial patterns to objects, like QR codes, can ease tasks such as object tracking, robot navigation, and conveying information (e.g., a label or a website link). However, these patterns require a physical application and they alter the object's appearance. Conversely, projected patterns can temporarily change the object's appearance, aiding tasks like 3D scanning and retrieving object textures and shading. However, projected patterns impede dynamic tasks like object tracking because they do not 'stick' to the object's surface. Or do they? This paper introduces a novel approach combining the advantages of projected and persistent physical patterns. Our system projects heat patterns using a laser beam (similar in spirit to a LIDAR), which a thermal camera observes and tracks. Such thermal patterns enable tracking poorly-textured objects whose tracking is highly challenging with standard cameras while not affecting the object's appearance or physical properties. To avail these thermal patterns in existing vision frameworks, we train a network to reverse heat diffusion's effects and remove inconsistent pattern points between different thermal frames. We prototyped and tested this approach on dynamic vision tasks like structure from motion, optical flow, and object tracking of everyday textureless objects.

关键词： 3d reconstruction heat Heat diffusion laser optical flow slam structure from motion thermal tracking

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BIOCLIP: A vision Foundation Model for the Tree of Life

BIOCLIP: A Vision Foundation Model for the Tree of Life

引用

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

作者： Stevens, Samuel Wu, Jiaman Thompson, Matthew J. Campolongo, Elizabeth G. Song, Chan Hee Carlyle, David Edward Dong, Li Dahdul, Wasila M. Stewart, Charles Berger-Wolf, Tanya Chao, Wei-Lun Su, Yu Ohio State Univ Columbus OH 43210 USA Microsoft Res Mountain View CA USA Univ Calif Irvine Irvine CA USA Rensselaer Polytech Inst Troy NY USA

ISBN: (纸本)9798350353006

Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an explosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions, contexts, and datasets. A vision model for general organismal biology questions on images is of timely need. To approach this, we curate and release TREEOFLIFE-10M, the largest and most diverse ML-ready dataset of biology images. We then develop BIOCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TREEOFLIFE-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on diverse fine-grained biology classification tasks and find that BIOCLIP consistently and substantially outperforms existing baselines (by 16% to 17% absolute). Intrinsic evaluation reveals that BIOCLIP has learned a hierarchical representation conforming to the tree of life, shedding light on its strong generalizability.(1)

关键词： computer vision evolutionary biology & ecology imageomics machine learning

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GRAM: Global Reasoning for Multi-Page VQA

GRAM: Global Reasoning for Multi-Page VQA

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

作者： Blau, Tsachi Fogel, Sharon Ronen, Roi Goltst, Alona Per, Shahar Tsi Ben Avraham, Elad Aberdam, Aviad Ganz, Roy Litman, Ron Technion Haifa Israel AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350353006

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our compression-transformer(C-Former), reducing the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

关键词： Document Understanding Long Sequence Processing vision Language Models

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ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization

ICON: Incremental CONfidence for Joint Pose and Radiance Fie...

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

作者： Wang, Weiyao Gleize, Pierre Tang, Hao Chen, Xingyu Liang, Kevin J. Feiszli, Matt Meta FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9798350353013;9798350353006

Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images. However, NeRF training requires accurate camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax this constraint, but they still often rely on decent initial poses which they can refine. Here we aim at removing the requirement for pose initialization. We present Incremental CONfidence (ICON), an optimization procedure for training NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate initial guess for poses. Further, ICON introduces "confidence": an adaptive measure of model quality used to dynamically reweight gradients. ICON relies on high-confidence poses to learn NeRF, and high-confidence 3D structure (as encoded by NeRF) to learn poses. We show that ICON, without prior pose initialization, achieves superior performance in both CO3D and HO3D versus methods which use SfM pose.

关键词： Three dimensional computer graphics

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

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

作者： Deb, Tonmoay Wang, Lichen Bessinger, Zachary Khosravan, Naji Penner, Eric Kang, Sing Bing Northwestern Univ Evanston IL 60208 USA Zillow Grp Seattle WA USA

ISBN: (纸本)9798350365474

This paper focuses on bridging the gap between natural language descriptions, 360 degrees panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell1. We first introduce an effective technique for extracting geometric information from ZInD's raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naive methods in both tasks, hence, can be considered as a complement to the naive to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： computer vision

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Sequential Modeling Enables Scalable Learning for Large vision Models

Sequential Modeling Enables Scalable Learning for Large Visi...

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

作者： Bail, Yutong Geng, Xinyang Mangalam, Karttikeya Bar, Amir Yuille, Alan L. Darrell, Trevor Malik, Jitendra Efros, Alexei A. UC Berkeley BAIR Berkeley CA 94720 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

We introduce a novel sequential modeling approach which enables learning a Large vision Model (LVM) without making use of any linguistic data. To do this, we define a common format, "visual sentences", in which we can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions with-out needing any meta-knowledge beyond the pixels. Once this wide variety of visual data (comprising 420 billion to-kens) is represented as sequences, the model can be trained to minimize a cross-entropy loss for next token prediction. By training across various scales of model architecture and data diversity, we provide empirical evidence that our models scale effectively. Many different vision tasks can be solved by designing suitable visual prompts at test time.

关键词： pretraining scaling Self-supervised Learning

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Towards Engineered Safe AI with Modular Concept Models

Towards Engineered Safe AI with Modular Concept Models

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

作者： Heidemann, Lena Kurzidem, Iwo Monnet, Maureen Roscher, Karsten Guennemann, Stephan Fraunhofer IKS Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9798350365474

The inherent complexity and uncertainty of Machine Learning (ML) makes it difficult for ML-based computer vision (CV) approaches to become prevalent in safety-critical domains like autonomous driving, despite their high performance. A crucial challenge in these domains is the safety assurance of ML-based systems. To address this, recent safety standardization in the automotive domain has introduced an ML safety lifecycle following an iterative development process. While this approach facilitates safety assurance, its iterative nature requires frequent adaptation and optimization of the ML function, which might include costly retraining of the ML model and is not guaranteed to converge to a safe AI solution. In this paper, we propose a modular ML approach which allows for more efficient and targeted measures to each of the modules and process steps. Each module of the modular concept model represents one visual concept and is aggregated with the other modules' outputs into a task output. The design choices of a modular concept model can be categorized into the selection of the concept modules, the aggregation of their output and the training of the concept modules. Using the example of traffic sign classification, we present each step of the involved design choices and the corresponding targeted measures to take in an iterative development process for engineering safe AI.

关键词： computer vision Concept Models Deep Neural Networks Explainable AI Interpretable Models Machine Learning ML Safety Modular Concept Models Modular Deep Learning Safe AI Safe ML

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VicTR: Video-conditioned Text Representations for Activity recognition

VicTR: Video-conditioned Text Representations for Activity R...

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

作者： Kahatapitiya, Kumara Arnab, Anurag Nagrani, Arsha Ryoo, Michael S. SUNY Stony Brook Stony Brook NY 11794 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

vision-Language models (VLMs) have excelled in the image-domain- especially in zero-shot settings- thanks to the availability of vast pretraining data (i.e., paired image-text samples). However for videos, such paired data is not as abundant. Therefore, video- VLMs are usually designed by adapting pretrained image- VLMs to the video-domain, instead of training from scratch. All such recipes rely on aug-menting visual embeddings with temporal information (i.e., image -+ video), often keeping text embeddings unchanged or even being discarded. In this paper, we argue the contrary, that better video- VLMs can be designed by focusing more on augmenting text, rather than visual information. More specifically, we introduce Video-conditioned Text Representations (Vi c TR): a form of text embeddings optimized w.r.t. vi-sual embeddings, creating a more-flexible contrastive latent space. Our model canfurther make use offreely-available semantic information, in the form of visually- grounded aux-iliary text (e.g. object or scene information). We evaluate our model on few-shot, zero-shot (HMDB-51, UCF-10l), short-form (Kinetics-400) and long-form (Charades) activ-ity recognition benchmarks, showing strong performance among video-VLMs.

关键词： Activity recognition Video Understanding Video-conditioned Text vision-language models

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