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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

共 52943 条记录，以下是151-160 订阅

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Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models

Strategies to Improve Real-World Applicability of Laparoscop...

引用

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

作者： Kolbinger, Fiona R. He, Jiangpeng Ma, Jinge Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support. Segmentation performance of such models is traditionally reported using metrics of overlap such as IoU. However, imbalanced and unrealistic representation of classes in the training data and suboptimal selection of reported metrics have the potential to skew nominal segmentation performance and thereby ultimately limit clinical translation. In this work, we systematically analyze the impact of class characteristics (i.e., organ size differences), training and test data composition (i.e., representation of positive and negative examples), and modeling parameters (i.e., foreground-to-background class weight) on eight segmentation metrics: accuracy, precision, recall, IoU, F1 score (Dice Similarity Coefficient), specificity, Hausdorff Distance, and Average Symmetric Surface Distance. Our findings support two adjustments to account for data biases in surgical data science: First, training on datasets that are similar to the clinical real-world scenarios in terms of class distribution, and second, class weight adjustments to optimize segmentation model performance with regard to metrics of particular relevance in the respective clinical setting.

关键词： Class Imbalance computer-Assisted Surgery Laparoscopic Surgery Semantic Segmentation Surgical Data Science

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SnAG: Scalable and Accurate Video Grounding

SnAG: Scalable and Accurate Video Grounding

引用

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

作者： Mu, Fangzhou Mo, Sicheng Li, Yin Univ Wisconsin Madison Madison WI 53706 USA

ISBN: (纸本)9798350353006

Temporal grounding of text descriptions in videos is a central problem in vision-language learning and video understanding. Existing methods often prioritize accuracy over scalability - they have been optimized for grounding only a few text queries within short videos, and fail to scale up to long videos with hundreds of queries. In this paper, we study the effect of cross-modal fusion on the scalability of video grounding models. Our analysis establishes late fusion as a more cost-effective fusion scheme for long-form videos with many text queries. Moreover, it leads us to a novel, video-centric sampling scheme for efficient training. Based on these findings, we present SnAG, a simple baseline for scalable and accurate video grounding. Without bells and whistles, SnAG is 43% more accurate and 1.5x faster than CONE, a state of the art for long-form video grounding on the challenging MAD dataset, while achieving highly competitive results on short videos. Our code is available at https://***/fmu2/snag_release.

关键词： Temporal Sentence Grounding Video understanding vision-Language Learning

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

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

引用

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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AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving

AIDE: An Automatic Data Engine for Object Detection in Auton...

引用

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

作者： Liang, Mingfu Sue, Jong-Chyi Schulter, Samuel Garg, Sparsh Zhao, Shiyu Wu, Ying Chandraker, Manmohan Northwestern Univ Evanston IL 60208 USA NEC Labs Amer Princeton NJ USA Rutgers State Univ New Brunswick NJ USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9798350353006

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. This process operates iteratively, allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.

关键词： Automatic Data Engine Autonomous Driving Continual Training Large Language Model Novel Object Detection vision Language Model

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Intrinsic Image Diffusion for Indoor Single-view Material Estimation

Intrinsic Image Diffusion for Indoor Single-view Material Es...

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

作者： Kocsis, Peter Sitzmann, Vincent Niessner, Matthias Tech Univ Munich Munich Germany MIT EECS Cambridge MA 02139 USA

ISBN: (纸本)9798350353013;9798350353006

We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue, we advocate for a probabilistic formulation, where instead of attempting to directly predict the true material properties, we employ a conditional generative model to sample from the solution space. Furthermore, we show that utilizing the strong learned prior of recent diffusion models trained on large-scale real-world images can be adapted to material estimation and highly improves the generalization to real images. Our method produces significantly sharper, more consistent, and more detailed materials, outperforming state-of-the-art methods by 1.5dB on PSNR and by 45% better FID score on albedo prediction. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.

关键词： Appearance Decompostion computer vision Deep Learning Diffusion Graphics Lighting Estimation Material Estimation

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Solving Masked Jigsaw Puzzles with Diffusion vision Transformers

Solving Masked Jigsaw Puzzles with Diffusion Vision Transfor...

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

作者： Liu, Jinyang Teshome, Wondmgezahu Ghimire, Sandesh Sznaier, Mario Camps, Octavia Northeastern Univ Boston MA 02115 USA Qualcomm San Diego CA USA

ISBN: (纸本)9798350353006

Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately, these methods face limitations in effectively solving puzzles with a large number of elements. In this paper, we propose JPDVT, an innovative approach that harnesses diffusion transformers to address this challenge. Specifically, we generate positional information for image patches or video frames, conditioned on their underlying visual content. This information is then employed to accurately assemble the puzzle pieces in their correct positions, even in scenarios involving missing pieces. Our method achieves state-of-the-art performance on several datasets.

关键词： data imputation diffusion models Solving puzzles

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BIOCLIP: A vision Foundation Model for the Tree of Life

BIOCLIP: A Vision Foundation Model for the Tree of Life

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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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MAFA: Managing False Negatives for vision-Language Pre-training

MAFA: Managing False Negatives for Vision-Language Pre-train...

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

作者： Byun, Jaeseok Kim, Dohoon Moon, Taesup Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ Dept ASRI INMC IPAI AIIS Seoul South Korea

ISBN: (纸本)9798350353006

We consider a critical issue of false negatives in vision-Language Pre-training (VLP), a challenge that arises from the inherent many-to-many correspondence of image-text pairs in large-scale web-crawled datasets. The presence of false negatives can impede achieving optimal performance and even lead to a significant performance drop. To address this challenge, we propose MAFA (MAnaging FAlse negatives), which consists of two pivotal components building upon the recently developed GRouped mIni-baTch sampling (GRIT) strategy: 1) an efficient connection mining process that identifies and converts false negatives into positives, and 2) label smoothing for the image-text contrastive (ITC) loss. Our comprehensive experiments verify the effectiveness of MAFA across multiple downstream tasks, emphasizing the crucial role of addressing false negatives in VLP, potentially even surpassing the importance of addressing false positives. In addition, the compatibility of MAFA with the recent BLIP-family model is also demonstrated. Code is available at https://***/jaeseokbyun/MAFA.

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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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A Perspective on Deep vision Performance with Standard Image and Video Codecs

A Perspective on Deep Vision Performance with Standard Image...

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

作者： Reich, Christoph Hahn, Oliver Cremers, Daniel Roth, Stefan Debnath, Biplob Tech Univ Darmstadt Darmstadt Germany Tech Univ Munich Munich Germany NEC Labs Amer Inc San Jose CA 95110 USA Hessian Ctr AI Hessian AI Darmstadt Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9798350365474

Resource-constrained hardware, such as edge devices or cell phones, often rely on cloud servers to provide the required computational resources for inference in deep vision models. However, transferring image and video data from an edge or mobile device to a cloud server requires coding to deal with network constraints. The use of standardized codecs, such as JPEG or H.264, is prevalent and required to ensure interoperability. This paper aims to examine the implications of employing standardized codecs within deep vision pipelines. We find that using JPEG and H.264 coding significantly deteriorates the accuracy across a broad range of vision tasks and models. For instance, strong compression rates reduce semantic segmentation accuracy by more than 80% in mIoU. In contrast to previous findings, our analysis extends beyond image and action classification to localization and dense prediction tasks, thus providing a more comprehensive perspective.

关键词： Image Classification Image Compression Object Detectio Optical Flow Estimation Semantic Segmentation Video Compression

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