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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11888 条记录，以下是81-90 订阅

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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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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

引用

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

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Samsung Res China Nanjing Peoples R China Nankai Univ Tianjin Peoples R China

ISBN: (纸本)9798350365474

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and cvpr 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： computer vision flare removal image restoration

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X-MIC: Cross-Modal Instance Conditioning for Egocentric Action Generalization

X-MIC: Cross-Modal Instance Conditioning for Egocentric Acti...

引用

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

作者： Kukleva, Anna Sener, Fadime Remelli, Edoardo Tekin, Bugra Sauser, Eric Schiele, Bernt Mal, Shugao Meta Real Labs Menlo Pk CA 94025 USA Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9798350353006

Lately, there has been growing interest in adapting vision-language models (VLMs) to image and third-person video classification due to their success in zero-shot recognition. However, the adaptation of these models to egocentric videos has been largely unexplored. To address this gap, we propose a simple yet effective cross-modal adaptation framework, which we call X-MIC. Using a video adapter, our pipeline learns to align frozen text embeddings to each egocentric video directly in the shared embedding space. Our novel adapter architecture retains and improves generalization of the pre-trained VLMs by disentangling learnable temporal modeling and frozen visual encoder. This results in an enhanced alignment of text embeddings to each egocentric video, leading to a significant improvement in cross-dataset generalization. We evaluate our approach on the Epic-Kitchens, Ego4D, and EGTEA datasets for fine-grained cross-dataset action generalization, demonstrating the effectiveness of our method.(1)

关键词： action recognition adapters egocentric generalization prompts VLMs adaptation zero-shot

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Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object recognition

Incorporating Geo-Diverse Knowledge into Prompting for Incre...

引用

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

作者： Buettner, Kyle Malakouti, Sina Li, Xiang Lorraine Kovashka, Adriana Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9798350353006

Existing object recognition models have been shown to lack robustness in diverse geographical scenarios due to domain shifts in design and context. Class representations need to be adapted to more accurately reflect an object concept under these shifts. In the absence of training data from target geographies, we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness. For this purpose, we explore the feasibility of probing a large language model for geography-based object knowledge, and we examine the effects of integrating knowledge into zero-shot and learnable soft prompting with CLIP. Within this exploration, we propose geography knowledge regularization to ensure that soft prompts trained on a source set of geographies generalize to an unseen target set. Accuracy gains over prompting baselines on DollarStreet while training only on Europe data are up to +2.8/1.2/1.6 on target data from Africa/Asia/Americas, and +4.6 overall on the hardest classes. Competitive performance is shown vs. few-shot target training, and analysis is provided to direct future study of geographical robustness.

关键词： Zero-shot learning

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Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video

Video2Game: Real-time, Interactive, Realistic and Browser-Co...

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

作者： Xia, Hongchi Lin, Zhi-Hao Ma, Wei-Chiu Wang, Shenlong Univ Illinois Champaign IL 61820 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Cornell Univ Ithaca NY USA

ISBN: (纸本)9798350353013;9798350353006

Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes. In this paper, we present Video2Game, a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components: (i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene;(ii) a mesh module that distills the knowledge from NeRF for faster rendering;and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline, one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time, but also build interactive games on top.

关键词： computer vision

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VLP: vision Language Planning for Autonomous Driving

VLP: Vision Language Planning for Autonomous Driving

引用

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

作者： Pan, Chenbin Yaman, Burhaneddin Nesti, Tommaso Mallik, Abhirup Allievi, Alessandro G. Velipasalar, Senem Rene, Liu Syracuse Univ Syracuse NY USA Bosch Res North Amer & Bosch Ctr Artificial Intel Sunnyvale CA 94085 USA

ISBN: (纸本)9798350353006

Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have recently achieved notable performance, through enhanced scene understanding, several key issues, including lack of reasoning, low generalization performance and long-tail scenarios, still need to be addressed. In this paper, we present VLP, a novel vision-Language-Planning framework that exploits language models to bridge the gap between linguistic understanding and autonomous driving. VLP enhances autonomous driving systems by strengthening both the source memory foundation and the self-driving car's contextual understanding. VLP achieves state-of-the-art end-to-end planning performance on the challenging NuScenes dataset by achieving 35.9% and 60.5% reduction in terms of average L2 error and collision rates, respectively, compared to the previous best method. Moreover, VLP shows improved performance in challenging long-tail scenarios and strong generalization capabilities when faced with new urban environments.

关键词： Visual languages

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Eclipse: Disambiguating Illumination and Materials using Unintended Shadows

Eclipse: Disambiguating Illumination and Materials using Uni...

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

作者： Verbin, Dor Mildenhall, Ben Hedman, Peter Barron, Jonathan T. Zickler, Todd Srinivasan, Pratul P. Google Res Mountain View CA 94043 USA Harvard Univ Cambridge MA USA

ISBN: (纸本)9798350353013;9798350353006

Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult, even when the object's 3D shape is known beforehand. This problem is especially challenging for diffuse objects: it is ill-conditioned because diffuse materials severely blur incoming light, and it is ill-posed because diffuse materials under high-frequency lighting can be indistinguishable from shiny materials under low-frequency lighting. We show that it is possible to recover precise materials and illumination-even from diffuse objects-by exploiting unintended shadows, like the ones cast onto an object by the photographer who moves around it. These shadows are a nuisance in most previous inverse rendering pipelines, but here we exploit them as signals that improve conditioning and help resolve material-lighting ambiguities. We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatiallyvarying materials, the surrounding illumination environment, and the shapes of the unseen light occluders who inadvertently cast shadows upon it.

关键词： computer Graphics Inverse Rendering Non-line-of-sight Imaging

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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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Contrasting intra-modal and ranking cross-modal hard negatives to enhance visio-linguistic compositional understanding

Contrasting intra-modal and ranking cross-modal hard negativ...

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

作者： Zhang, Le Awal, Rabiul Agrawal, Aishwarya Mila Quebec AI Inst Montreal PQ Canada Univ Montreal Montreal PQ Canada Canada CIFAR AI Chair Montreal PQ Canada

ISBN: (纸本)9798350353006

vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream tasks such as zero-shot image classification, image-text retrieval, and text-to-image generation. However, the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally, the current contrastive learning objective fails to focus on fine-grained grounding components like relations, actions, and attributes, resulting in "bag-of-words" representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP, our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks.(1)

关键词： compositional understanding contrastive learning vision-language models

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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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