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共 11890 条记录，以下是301-310 订阅

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Super-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of SO(3)

Super-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of S...

引用

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

作者： Alexa, Marc TU Berlin Berlin Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Super-Fibonacci spirals are an extension of Fibonacci spirals, enabling fast generation of an arbitrary but fixed number of 3D orientations. The algorithm is simple and fast. A comprehensive evaluation comparing to other methods shows that the generated sets of orientations have low discrepancy, minimal spurious components in the power spectrum, and almost identical Voronoi volumes. This makes them useful for a variety of applications, in particular Monte Carlo sampling.

关键词： computer vision Spirals Three-dimensional displays Monte Carlo methods Tracking Pose estimation Optimization methods

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Test-Time Adaptation for Depth Completion

Test-Time Adaptation for Depth Completion

引用

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

作者： Park, Hyoungseob Gupta, Anjali Wong, Alex Yale Vision Lab New Haven CT 06501 USA

ISBN: (纸本)9798350353006

It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap, such as domain adaptation (DA), may require the source data on which the model was trained (often not available), while others, i.e., source-free DA, require many passes through the testing data. We propose an online test-time adaptation method for depth completion, the task of inferring a dense depth map from a single image and associated sparse depth map, that closes the performance gap in a single pass. We first present a study on how the domain shift in each data modality affects model performance. Based on our observations that the sparse depth modality exhibits a much smaller covariate shift than the image, we design an embedding module trained in the source domain that preserves a mapping from features encoding only sparse depth to those encoding image and sparse depth. During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain. We evaluate our method on indoor and outdoor scenarios and show that it improves over baselines by an average of 21.1%. Code available at ***/seobbro/TTA-depth-completion.

关键词： 3D computer vision Depth completion Depth estimation Multi-modal fusion Test time adaptation

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Latent-based Diffusion Model for Long-tailed recognition

Latent-based Diffusion Model for Long-tailed Recognition

引用

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

作者： Han, Pengxiao Ye, Changkun Zhou, Jieming Zhang, Jing Hong, Jie Li, Xuesong Australian Natl Univ Canberra ACT Australia CSIRO Canberra ACT Australia

ISBN: (纸本)9798350365474

Long-tailed imbalance distribution is a common issue in practical computer vision applications. Previous works proposed methods to address this problem, which can be categorized into several classes: re-sampling, re-weighting, transfer learning, and feature augmentation. In recent years, diffusion models have shown an impressive generation ability in many sub-problems of deep computer vision. However, its powerful generation has not been explored in long-tailed problems. We propose a new approach, the Latent-based Diffusion Model for Long-tailed recognition (LDMLR), as a feature augmentation method to tackle the issue. First, we encode the imbalanced dataset into features using the baseline model. Then, we train a Denoising Diffusion Implicit Model (DDIM) using these encoded features to generate pseudo-features. Finally, we train the classifier using the encoded and pseudo-features from the previous two steps. The model's accuracy shows an improvement on the CIFAR-LT and ImageNet-LT datasets by using the proposed method.

关键词： diffusion model imbalance distribution long-tailed recognition

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EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object recognition

EventDance: Unsupervised Source-free Cross-modal Adaptation ...

引用

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

作者： Zheng, Xu Wang, Lin HKUST GZ AI Thrust Guangzhou Peoples R China HKUST Dept CSE Guangzhou Peoples R China

ISBN: (纸本)9798350353006

In this paper, we make the first attempt at achieving the cross-modal (i.e., image-to-events) adaptation for event-based object recognition without accessing any labeled source image data owning to privacy and commercial issues. Tackling this novel problem is non-trivial due to the novelty of event cameras and the distinct modality gap between images and events. In particular, as only the source model is available, a hurdle is how to extract the knowledge from the source model by only using the unlabeled target event data while achieving knowledge transfer. To this end, we propose a novel framework, dubbed EventDance for this unsupervised source-free cross-modal adaptation problem. Importantly, inspired by event-to-video reconstruction methods, we propose a reconstruction-based modality bridging (RMB) module, which reconstructs intensity frames from events in a self-supervised manner. This makes it possible to build up the surrogate images to extract the knowledge (i.e., labels) from the source model. We then propose a multi-representation knowledge adaptation ( MKA) module that transfers the knowledge to target models learning events with multiple representation types for fully exploring the spatiotemporal information of events. The two modules connecting the source and target models are mutually updated so as to achieve the best performance. Experiments on three benchmark datasets with two adaption settings show that EventDance is on par with prior methods utilizing the source data.

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Part-aware Unified Representation of Language and Skeleton for Zero-shot Action recognition

Part-aware Unified Representation of Language and Skeleton f...

引用

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

作者： Zhu, Anqi Ke, Qiuhong Gong, Mingming Bailey, James Univ Melbourne Melbourne Vic Australia Monash Univ Parkville Vic 3052 Australia Wellington Rd Clayton Vic 3800 Australia

ISBN: (纸本)9798350353006

While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored. In this paper, we argue that relying solely on aligning label-level semantics and global skeleton features is insufficient to effectively transfer locally consistent visual knowledge from seen to unseen classes. To address this limitation, we introduce Part-aware Unified Representation between Language and Skeleton (PURLS) to explore visual-semantic alignment at both local and global scales. PURLS introduces a new prompting module and a novel partitioning module to generate aligned textual and visual representations across different levels. The former leverages a pre-trained GPT-3 to infer refined descriptions of the global and local (body-part-based and temporal-interval-based) movements from the original action labels. The latter employs an adaptive sampling strategy to group visual features from all body joint movements that are semantically relevant to a given description. Our approach is evaluated on various skeleton/language backbones and three large-scale datasets, i.e., NTU-RGB+D 60, NTU-RGB+D 120, and a newly curated dataset Kinetics-skeleton 200. The results showcase the universality and superior performance of PURLS, surpassing prior skeleton-based solutions and standard baselines from other domains. The source codes can be accessed at https://***/azzh1/PURLS.

关键词： action recognition computer vision contrastive learning large language model representation learning skeleton action recognition zero-shot learning

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OMNIPARSER: A Unified Framework for Text Spotting, Key Information Extraction and Table recognition

OMNIPARSER: A Unified Framework for Text Spotting, Key Infor...

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

作者： Wan, Jianqiang Song, Sibo Yu, Wenwen Liu, Yuliang Cheng, Wenqing Huang, Fei Bai, Xiang Yao, Cong Yang, Zhibo Alibaba Grp Hangzhou Peoples R China Huazhong Univ Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However, due to the diversified targets and heterogeneous schemas, previous works usually design task-specific architectures and objectives for individual tasks, which inadvertently leads to modal isolation and complex workflow. In this paper, we propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically, we devise a universal model, called OmniParser, which can simultaneously handle three typical visually-situated text parsing tasks: text spotting, key information extraction, and table recognition. In OmniParser, all tasks share the unified encoder-decoder architecture, the unified objective: point-conditioned text generation, and the unified input&output representation: prompt & structured sequences. Extensive experiments demonstrate that the proposed OmniParser achieves state-of-the-art (SOTA) or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks, despite its unified, concise design. The code is available at AdvancedLiterateMachinery.

关键词： key information extraction table recognition text spotting unified framework

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HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces

HybridNeRF: Efficient Neural Rendering via Adaptive Volumetr...

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

作者： Turki, Haithem Agrawal, Vasu Bulo, Samuel Rota Porzi, Lorenzo Kontschieder, Peter Ramanan, Deva Zollhofer, Michael Richardt, Christian Meta Real Labs Menlo Pk CA 94025 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. One reason is that they make use of volume rendering, thus requiring many samples (and model queries) per ray at render time. Although this representation is flexible and easy to optimize, most real-world objects can be modeled more efficiently with surfaces instead of volumes, requiring far fewer samples per ray. This observation has spurred considerable progress in surface representations, such as signed distance functions, but these may struggle to model semi-opaque and thin structures. We propose a method, HybridNeRF, that leverages the strengths of both representations by rendering most objects as surfaces while modeling the (typically) small fraction of challenging regions volumetrically. We evaluate HybridNeRF against the challenging Eyeful Tower dataset [38] along with other commonly used view synthesis datasets. When comparing to state-of-the-art baselines, including recent rasterization-based approaches, we improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2K -> 2K). Project page: https://***/hybrid-nerf/.

关键词： 3d reconstruction computer vision machine learning neural radiance fields neural rendering novel view synthesis

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Atom-Level Optical Chemical Structure recognition with Limited Supervision

Atom-Level Optical Chemical Structure Recognition with Limit...

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

作者： Oldenhof, Martijn De Brouwer, Edward Arany, Adam Moreau, Yves Katholieke Univ Leuven ESAT STADIUS Leuven Belgium Yale Univ New Haven CT USA

ISBN: (纸本)9798350353006

Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do not typically generalize well, and show diminished effectiveness when confronted with domains where data is sparse, or costly to generate, such as hand-drawn molecule images. To address this limitation, we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches, our method provides atom-level localization, and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking, we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency, accuracy, and atom-level entity prediction.

关键词： graph recognition object detection OCSR weak supervision

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Distilling vision-Language Models on Millions of Videos

Distilling Vision-Language Models on Millions of Videos

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

作者： Zhao, Yue Zhao, Long Zhou, Xingyi Wu, Jialin Chu, Chun-Te Mia, Hui Schroff, Florian Adam, Hartwig Liu, Ting Gong, Boqing Krahenbuhl, Philipp Yuan, Liangzhe Google Res Mountain View CA 94043 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350353006

The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply is not enough human- curated video-text data available. We thus resort to fine-tuning a video-language model from a strong image-language baseline with synthesized instructional data. The resulting video model by video-instruction-tuning (VIIT) is then used to auto-label millions of videos to generate high-quality captions. We show the adapted video-language model performs well on a wide range of video-language benchmarks. For instance, it surpasses the best prior result on open-ended NExT-QA by 2.8%. Besides, our model generates detailed descriptions for previously unseen videos, which provide better textual supervision than existing methods. Experiments show that a video-language dual-encoder model contrastively trained on these auto-generated captions is 3.8% better than the strongest baseline that also leverages vision-language models. Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%. As a side product, we generate the largest video capation dataset to date.

关键词： Video analysis

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ParamISP: Learned Forward and Inverse ISPs using Camera Parameters

ParamISP: Learned Forward and Inverse ISPs using Camera Para...

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

作者： Kim, Woohyeok Kim, Geonu Lee, Junyong Lee, Seungyong Baek, Seung-Hwan Cho, Sunghyun POSTECH Pohang South Korea Samsung AI Ctr Toronto Toronto ON Canada Samsung Toronto ON Canada

ISBN: (纸本)9798350353006

RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated, enabling physically-meaningful RAW-level image processing on input sRGB images. However, existing learning-based ISP methods fail to handle the large variations in the ISP processes with respect to camera parameters such as ISO and exposure time, and have limitations when used for various applications. In this paper, we propose ParamISP, a learning-based method for forward and inverse conversion between sRGB and RAW images, that adopts a novel neural-network module to utilize camera parameters, which is dubbed as ParamNet. Given the camera parameters provided in the EXIF data, ParamNet converts them into a feature vector to control the ISP networks. Extensive experiments demonstrate that ParamISP achieve superior RAWand sRGB reconstruction results compared to previous methods and it can be effectively used for a variety of applications such as deblurring dataset synthesis, raw deblurring, HDR reconstruction, and camera-to-camera transfer.

关键词： Low-level vision

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