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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是221-230 订阅

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Affordance Grounding from Demonstration Video to Target Image

Affordance Grounding from Demonstration Video to Target Imag...

引用

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

作者： Chen, Joya Gao, Difei Lin, Kevin Qinghong Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore

ISBN: (纸本)9798350301298

Humans excel at learning from expert demonstrations and solving their own problems. To equip intelligent robots and assistants, such as AR glasses, with this ability, it is essential to ground human hand interactions (i.e., affordances) from demonstration videos and apply them to a target image like a user's AR glass view. This video-to-image affordance grounding task is challenging due to (1) the need to predict fine-grained affordances, and (2) the limited training data, which inadequately covers video-image discrepancies and negatively impacts grounding. To tackle them, we propose Affordance Transformer (Afformer), which has a fine-grained transformer-based decoder that gradually refines affordance grounding. Moreover, we introduce Mask Affordance Hand (MaskAHand), a self-supervised pre-training technique for synthesizing video-image data and simulating context changes, enhancing affordance grounding across video-image discrepancies. Afformer with MaskAHand pre-training achieves state-of-the-art performance on multiple benchmarks, including a substantial 37% improvement on the OPRA dataset. Code is made available at https://***/showlab/afformer.

关键词： vision applications and systems

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Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

Cascaded Local Implicit Transformer for Arbitrary-Scale Supe...

引用

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

作者： Chen, Hao-Wei Xu, Yu-Syuan Hong, Min-Fong Tsai, Yi-Min Kuo, Hsien-Kai Lee, Chun-Yi Natl Tsing Hua Univ Elsa Lab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggregate local features and a local frequency encoding block to combine positional encoding with Fourier domain information for constructing high-resolution images. To further improve representative power, we propose a Cascaded LIT (CLIT) that exploits multi-scale features, along with a cumulative training strategy that gradually increases the upsampling scales during training. We have conducted extensive experiments to validate the effectiveness of these components and analyze various training strategies. The qualitative and quantitative results demonstrate that LIT and CLIT achieve favorable results and outperform the prior works in arbitrary super-resolution tasks.

关键词： Low-level vision

来源：评论

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MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection

MetaFusion: Infrared and Visible Image Fusion via Meta-Featu...

引用

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

作者： Zhao, Wenda Xie, Shigeng Zhao, Fan He, You Lu, Huchuan Dalian Univ Technol Dalian Peoples R China Liaoning Normal Univ Dalian Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Fusing infrared and visible images can provide more texture details for subsequent object detection task. Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion. Thus, a joint fusion and detection learning to use their mutual promotion is attracting more attention. However, the feature gap between these two different-level tasks hinders the progress. Addressing this issue, this paper proposes an infrared and visible image fusion via meta-feature embedding from object detection. The core idea is that meta-feature embedding model is designed to generate object semantic features according to fusion network ability, and thus the semantic features are naturally compatible with fusion features. It is optimized by simulating a meta learning. Moreover, we further implement a mutual promotion learning between fusion and detection tasks to improve their performances. Comprehensive experiments on three public datasets demonstrate the effectiveness of our method. Code and model are available at: https://***/wdzhao123/MetaFusion.

关键词： Low-level vision

来源：评论

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Deep Factorized Metric Learning

Deep Factorized Metric Learning

引用

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

作者： Wang, Chengkun Zheng, Wenzhao Li, Junlong Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks. While existing methods approach this goal by learning an ensemble of embeddings with diverse objectives, the backbone network still receives a mix of all the training signals. Differently, we propose a deep factorized metric learning (DFML) method to factorize the training signal and employ different samples to train various components of the backbone network. We factorize the network to different sub-blocks and devise a learnable router to adaptively allocate the training samples to each sub-block with the objective to capture the most information. The metric model trained by DFML capture different characteristics with different sub-blocks and constitutes a generalizable metric when using all the subblocks. The proposed DFML achieves state-of-the-art performance on all three benchmarks for deep metric learning including CUB-200-2011, Cars196, and Stanford Online Products. We also generalize DFML to the image classification task on ImageNet-1K and observe consistent improvement in accuracy/computation trade-off. Specifically, we improve the performance of ViT-B on ImageNet (+0.2% accuracy) with less computation load (-24% FLOPs). (1)

关键词： detection recognition: Categorization retrieval

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Automatic High Resolution Wire Segmentation and Removal

Automatic High Resolution Wire Segmentation and Removal

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

作者： Chiu, Mang Tik Zhang, Xuaner Wei, Zijun Zhou, Yuqian Shechtman, Eli Barnes, Connelly Lin, Zhe Kainz, Florian Amirghodsi, Sohrab Shi, Humphrey UIUC Champaign IL 61801 USA Adobe San Jose CA USA Univ Oregon Eugene OR USA

ISBN: (纸本)9798350301298

Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. The manual process of precisely segmenting and removing them is extremely tedious and may take up hours, especially on high-resolution photos where wires may span the entire space. In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds. We observe several unique challenges: wires are thin, lengthy, and sparse. These are rare properties of subjects that common segmentation tasks cannot handle, especially in high-resolution images. We thus propose a two-stage method that leverages both global and local contexts to accurately segment wires in high-resolution images efficiently, and a tile-based inpainting strategy to remove the wires given our predicted segmentation masks. We also introduce the first wire segmentation benchmark dataset, WireSegHR. Finally, we demonstrate quantitatively and qualitatively that our wire clean-up system enables fully automated wire removal with great generalization to various wire appearances.

关键词： vision applications and systems

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Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

Multimodality Helps Unimodality: Cross-Modal Few-Shot Learni...

引用

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

作者： Lin, Zhiqiu Yu, Samuel Kuang, Zhiyi Pathak, Deepak Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350301298

The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better visual dog classifier by reading about dogs and listening to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification. Project site at link.

关键词： detection recognition: Categorization retrieval

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Metadata-Based RAW Reconstruction via Implicit Neural Functions

Metadata-Based RAW Reconstruction via Implicit Neural Functi...

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

作者： Li, Leyi Qiao, Huijie Ye, Qi Yang, Qinmin Zhejiang Univ Hangzhou Peoples R China Chinese Acad Sci Beijing Peoples R China Key Lab CS Hangzhou Peoples R China AUS Zhejiang Prov Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Many low-level computer vision tasks are desirable to utilize the unprocessed RAW image as input, which remains the linear relationship between pixel values and scene radiance. Recent works advocate to embed the RAW image samples into sRGB images at capture time, and reconstruct the RAW from sRGB by these metadata when needed. However, there still exist some limitations in making full use of the metadata. In this paper, instead of following the perspective of sRGB-to-RAW mapping, we reformulate the problem as mapping the 2D coordinates of the metadata to its RAW values conditioned on the corresponding sRGB values. With this novel formulation, we propose to reconstruct the RAW image with an implicit neural function, which achieves significant performance improvement (more than 10dB average PSNR) only with the uniform sampling. Compared with most deep learning-based approaches, our method is trained in a self-supervised way that requiring no pre-training on different camera ISPs. We perform further experiments to demonstrate the effectiveness of our method, and show that our framework is also suitable for the task of guided super-resolution.

关键词： Low-level vision

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Unified Keypoint-based Action recognition Framework via Structured Keypoint Pooling

Unified Keypoint-based Action Recognition Framework via Stru...

引用

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

作者： Hachiume, Ryo Sato, Fumiaki Sekii, Taiki Konica Minolta Inc Tokyo Japan

ISBN: (纸本)9798350301298

This paper simultaneously addresses three limitations associated with conventional skeleton-based action recognition;skeleton detection and tracking errors, poor variety of the targeted actions, as well as person-wise and frame-wise action recognition. A point cloud deep-learning paradigm is introduced to the action recognition, and a unified framework along with a novel deep neural network architecture called Structured Keypoint Pooling is proposed. The proposed method sparsely aggregates keypoint features in a cascaded manner based on prior knowledge of the data structure (which is inherent in skeletons), such as the instances and frames to which each keypoint belongs, and achieves robustness against input errors. Its less constrained and tracking-free architecture enables time-series keypoints consisting of human skeletons and nonhuman object contours to be efficiently treated as an input 3D point cloud and extends the variety of the targeted action. Furthermore, we propose a Pooling-Switching Trick inspired by Structured Keypoint Pooling. This trick switches the pooling kernels between the training and inference phases to detect person-wise and frame-wise actions in a weakly supervised manner using only video-level action labels. This trick enables our training scheme to naturally introduce novel data augmentation, which mixes multiple point clouds extracted from different videos. In the experiments, we comprehensively verify the effectiveness of the proposed method against the limitations, and the method outperforms state-of-the-art skeleton-based action recognition and spatio-temporal action localization methods.

关键词： Video: Action and event understanding

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Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints

Enhancing Deformable Local Features by Jointly Learning to D...

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

作者： Potje, Guilherme Cadar, Felipe Araujo, Andre Martins, Renato Nascimento, Erickson R. Univ Fed Minas Gerais Belo Horizonte Brazil Google Res New York NY USA Univ Bourgogne Dijon France Univ Lorraine LORIA Inria Thionville France Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that images undergo affine transformations, disregarding more complicated effects such as non-rigid deformations. Furthermore, incipient works tailored for non-rigid correspondence still rely on keypoint detectors designed for rigid transformations, hindering performance due to the limitations of the detector. We propose DALF (Deformation-Aware Local Features), a novel deformation-aware network for jointly detecting and describing keypoints, to handle the challenging problem of matching deformable surfaces. All network components work cooperatively through a feature fusion approach that enforces the descriptors' distinctiveness and invariance. Experiments using real deforming objects showcase the superiority of our method, where it delivers 8% improvement in matching scores compared to the previous best results. Our approach also enhances the performance of two real-world applications: deformable object retrieval and non-rigid 3D surface registration. Code for training, inference, and applications are publicly available at verlab. ***/descriptors/dalf_cvpr23.

关键词： Low-level vision

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Probabilistic Prompt Learning for Dense Prediction

Probabilistic Prompt Learning for Dense Prediction

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

作者： Kwon, Hyeongjun Song, Taeyong Jeong, Somi Kim, Jin Jang, Jinhyun Sohn, Kwanghoon Yonsei Univ Seoul South Korea Hyundai Motor Co R&D Div Seoul South Korea NAVER LABS Seongnam South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9798350301298

Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models. However, this approach results in limited performance for dense prediction tasks that require handling more complex and diverse objects, since a single and deterministic description cannot sufficiently represent the entire image. In this paper, we present a novel probabilistic prompt learning to fully exploit the vision-language knowledge in dense prediction tasks. First, we introduce learnable class-agnostic attribute prompts to describe universal attributes across the object class. The attributes are combined with class information and visual-context knowledge to define the class-specific textual distribution. Text representations are sampled and used to guide the dense prediction task using the probabilistic pixel-text matching loss, enhancing the stability and generalization capability of the proposed method. Extensive experiments on different dense prediction tasks and ablation studies demonstrate the effectiveness of our proposed method.

关键词： and reasoning language vision

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