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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

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GM-DETR: Generalized Muiltispectral DEtection TRansformer with Efficient Fusion Encoder for Visible-Infrared Detection

GM-DETR: Generalized Muiltispectral DEtection TRansformer wi...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Yiming Xiao Fanman Meng Qingbo Wu Linfeng Xu Mingzhou He Hongliang Li University of Electronic Science and Technology of China China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multispectral object detection based on RGB and IR achieves improved accurate and robust performance by integrating complementary information from different modalities. However, existing methods predominantly focus on effectively fusing information from both modalities to enhance detection performance, and rarely study the diversified utilization of RGB and IR data and explore the adaptability of the model to practical application scenarios. We first analyze the utilization of datasets for multispectral object detection, and compare their testing performance. To better leverage datasets and address more generalized model application scenarios, we propose a Generalized Multispectral DEtection TRansformer (GM-DETR) with a two-stage training strategy. Specifically, we design the Modality-Specific Feature Interaction (MSFI) module to extract the high-level information from RGB and IR, and propose the Cross-Modality-Scale feature Fusion (CMSF) module for fusing RGB and IR modalities, which performs multi-scale cross-modalities fusion. Our GM-DETR achieves state-of-the-art performance on FLIR and LLVIP benchmark datasets.

关键词： Training Adaptation models computer vision conferences Object detection Benchmark testing Feature extraction

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Detecting Human-Object Interaction via Fabricated Compositional Learning

Detecting Human-Object Interaction via Fabricated Compositio...

引用

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

作者： Hou, Zhi Yu, Baosheng Qiao, Yu Peng, Xiaojiang Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Human-Object Interaction (HOI) detection, inferring the relationships between human and objects from images/videos, is a fundamental task for high-level scene understanding. However, HOI detection usually suffers from the open long-tailed nature of interactions with objects, while human has extremely powerful compositional perception ability to cognize rare or unseen HOI samples. Inspired by this, we devise a novel HOI compositional learning framework, termed as Fabricated Compositional Learning (FCL), to address the problem of open long-tailed HOI detection. Specifically, we introduce an object fabricator to generate effective object representations, and then combine verbs and fabricated objects to compose new HOI samples. With the proposed object fabricator, we are able to generate large-scale HOI samples for rare and unseen categories to alleviate the open long-tailed issues in HOI detection. Extensive experiments on the most popular HOI detection dataset, HICO-DET, demonstrate the effectiveness of the proposed method for imbalanced HOI detection and significantly improve the state-of-the-art performance on rare and unseen HOI categories. Code is available at https://***/zhihou7/HOI-CL.

关键词： computer vision Codes Benchmark testing pattern recognition Task analysis

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Neural Fields for Co-Reconstructing 3D Objects from Incidental 2D Data

Neural Fields for Co-Reconstructing 3D Objects from Incident...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Dylan Campbell Eldar Insafutdinov João F. Henriques Andrea Vedaldi Australian National University University of Oxford

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We ask whether 3D objects can be reconstructed from real world data collected for some other purpose, such as autonomous driving or augmented reality, thus inferring objects only incidentally. 3D reconstruction from incidental data is a major challenge because, in addition to significant noise, only a few views of each object are observed, which are insufficient for reconstruction. We approach this problem as a co-reconstruction task, where multiple objects are reconstructed together, learning shape and appearance priors for regularization. In order to do so, we introduce a neural radiance field that is conditioned via an attention mechanism on the identity of the individual objects. We further disentangle shape from appearance and diffuse color from specular color via an asymmetric two-stream network, which factors shared information from instance-specific details. We demonstrate the ability of this method to reconstruct full 3D objects from partial, incidental observations in autonomous driving and other datasets.

关键词： computer vision Three-dimensional displays Shape Image color analysis conferences Noise Neural radiance field

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Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

Simple Copy-Paste is a Strong Data Augmentation Method for I...

引用

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

作者： Ghiasi, Golnaz Cui, Yin Srinivas, Aravind Qian, Rui Lin, Tsung-Yi Cubuk, Ekin D. Le, Quoc, V Zoph, Barret Google Res Brain Team Mountain View CA 94043 USA Univ Calif Berkeley Berkeley CA USA Cornell Univ Ithaca NY 14853 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation (e.g.,[13, 12]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines. Furthermore, we show Copy-Paste is additive with semi-supervised methods that leverage extra data through pseudo labeling (e.g. self-training). On COCO instance segmentation, we achieve 49.1 mask AP and 57.3 box AP an improvement of +0.6 mask AP and +1.5 box AP over the previous state-of-the-art. We further demonstrate that Copy-Paste can lead to significant improvements on the LVIS benchmark. Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3.6 mask AP on rare categories.(1)

关键词： Training Image segmentation computer vision Visualization Additives Systematics Computational modeling

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CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation

CoCosNet v2: Full-Resolution Correspondence Learning for Ima...

引用

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

作者： Zhou, Xingran Zhang, Bo Zhang, Ting Zhang, Pan Bao, Jianmin Chen, Dong Zhang, Zhongfei Wen, Fang Zhejiang Univ Hangzhou Zhejiang Peoples R China Microsoft Res Asia Beijing Peoples R China SUNY Binghamton Binghamton NY 13902 USA USTC Hefei Anhui Peoples R China

ISBN: (纸本)9781665445092

We present the full-resolution correspondence learning for cross-domain images, which aids image translation. We adopt a hierarchical strategy that uses the correspondence from coarse level to guide the fine levels. At each hierarchy, the correspondence can be efficiently computed via PatchMatch that iteratively leverages the matchings from the neighborhood. Within each PatchMatch iteration, the ConvGRU module is employed to refine the current correspondence considering not only the matchings of larger context but also the historic estimates. The proposed CoCosNet v2, a GRU-assisted PatchMatch approach, is fully differentiable and highly efficient. When jointly trained with image translation, full-resolution semantic correspondence can be established in an unsupervised manner, which in turn facilitates the exemplar-based image translation. Experiments on diverse translation tasks show that CoCosNet v2 performs considerably better than state-of-the-art literature on producing high-resolution images.

关键词： computer vision Image resolution Semantics Iterative algorithms pattern recognition Task analysis

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Affordance Transfer Learning for Human-Object Interaction Detection

Affordance Transfer Learning for Human-Object Interaction De...

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

作者： Hou, Zhi Yu, Baosheng Qiao, Yu Peng, Xiaojiang Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Reasoning the human-object interactions (HOI) is essential for deeper scene understanding, while object affordances (or functionalities) are of great importance for human to discover unseen HOIs with novel objects. Inspired by this, we introduce an affordance transfer learning approach to jointly detect HOIs with novel object and recognize affordances. Specifically, HOI representations can be decoupled into a combination of affordance and object representations, making it possible to compose novel interactions by combining affordance representations and novel object representations from additional images, i.e. transferring the affordance to novel objects. With the proposed affordance transfer learning, the model is also capable of inferring the affordances of novel objects from known affordance representations. The proposed method can thus be used to 1) improve the performance of HOI detection, especially for the HOIs with unseen objects;and 2) infer the affordances of novel objects. Experimental results on two datasets, HICO-DET and HOI-COCO (from V-COCO), demonstrate significant improvements over recent state-of-the-art methods for HOI detection and object affordance detection.

关键词： computer vision Codes Affordances Computational modeling Transfer learning Cognition pattern recognition

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Invertible Denoising Network: A Light Solution for Real Noise Removal

Invertible Denoising Network: A Light Solution for Real Nois...

引用

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

作者： Liu, Yang Qin, Zhenyue Anwar, Saeed Ji, Pan Kim, Dongwoo Caldwell, Sabrina Gedeon, Tom Australian Natl Univ Canberra ACT Australia CSIRO Data61 Canberra ACT Australia OPPO US Res Palo Alto CA USA GSAI POSTECH Pohang South Korea

ISBN: (纸本)9781665445092

Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying invertible models to remove noise is challenging because the input is noisy, and the reversed output is clean, following two different distributions. We propose an invertible denoising network, InvDN, to address this challenge. InvDN transforms the noisy input into a low-resolution clean image and a latent representation containing noise. To discard noise and restore the clean image, InvDN replaces the noisy latent representation with another one sampled from a prior distribution during reversion. The denoising performance of InvDN is better than all the existing competitive models, achieving a new state-of-the-art result for the SIDD dataset while enjoying less run time. Moreover, the size of InvDN is far smaller, only having 4.2% of the number of parameters compared to the most recently proposed DANet. Further, via manipulating the noisy latent representation, InvDN is also able to generate noise more similar to the original one. Our code is available at: https://***/Yang-Liu1082/***.

关键词： computer vision Codes Computational modeling Noise reduction Transforms Image restoration pattern recognition

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Style-based Point Generator with Adversarial Rendering for Point Cloud Completion

Style-based Point Generator with Adversarial Rendering for P...

引用

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

作者： Xie, Chulin Wang, Chuxin Zhang, Bo Yang, Hao Chen, Dong Wen, Fang Univ Illinois Champaign IL 61820 USA Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to fully exploit the local structures as well as the global shape in point features. Secondly, we observe that the concatenation manner used by vanilla foldings limits its potential of generating a complex and faithful shape. Enlightened by the success of StyleGAN, we regard the shape feature as style code that modulates the normalization layers during the folding, which considerably enhances its capability. Thirdly, we realize that existing point supervisions, e.g., Chamfer Distance or Earth Mover's Distance, cannot faithfully reflect the perceptual quality of the reconstructed points. To address this, we propose to project the completed points to depth maps with a differentiable renderer and apply adversarial training to advocate the perceptual realism under different viewpoints. Comprehensive experiments on ShapeNet and KITTI prove the effectiveness of our Method, which achieves state-of the-art quantitative performance while offering superior visual quality.

关键词： Training Earth Visualization Shape Image edge detection Rendering (computer graphics) Feature extraction

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High Quality Reference Feature for Two Stage Bracketing Image Restoration and Enhancement

High Quality Reference Feature for Two Stage Bracketing Imag...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Xiaoxia Xing HyunHee Park Fan Wang Ying Zhang Sejun Song Changho Kim Xiangyu Kong Samsung R&D Institute China-Beijing Department of Camera Innovation Group Samsung Electronics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In a low-light environment, it is difficult to obtain high-quality or high-resolution images with sharp details and high dynamic range (HDR) without noise or blur. To solve this problem, the Bracketing Image Restoration and Enhancement integrates Dnoise, Deblur, HDR Reconstruction, and Super Resolution techniques into a unified framework. However, we find that most methods select the image that aligns with GT as the reference image. Since the details of the reference image are not good enough, seriously affects the feature fusion, which finally leads to details being blurred. To generate a high dynamic range and a high-quality image, we propose a two-stage Bracketing method named RT-IRE. In the first stage, we generate the high-quality reference feature to guide feature fusion, remove the degradation, and reconstruct HDR to get coarse results. The second stage learns the residuals between the coarse result and the GT, which further enhances and generates details. Extensive experiments show the effectiveness of the proposed module. In particular, RT-IRE won two champions in the NTIRE 2024 Bracketing Image Restoration and Enhancement Challenge.

关键词： Degradation computer vision Accuracy conferences Noise Superresolution Image restoration

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Image-to-image Translation via Hierarchical Style Disentanglement

Image-to-image Translation via Hierarchical Style Disentangl...

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

作者： Li, Xinyang Zhang, Shengchuan Hu, Jie Cao, Liujuan Hong, Xiaopeng Mao, Xudong Huang, Feiyue Wu, Yongjian Ji, Rongrong Xiamen Univ Sch Informat Xiamen Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xiamen Univ Tencent Youtu Lab Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China

ISBN: (纸本)9781665445092

Recently, image-to-image translation has made significant progress in achieving both multi-label (i.e., translation conditioned on different labels) and multi-style (i.e., generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated by involving uncontrolled manipulations to the translation results. In this paper, we propose Hierarchical Style Disentanglement (HiSD) to address this issue. Specifically, we organize the labels into a hierarchical tree structure, in which independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. Correspondingly, a new translation process is designed to adapt the above structure, in which the styles are identified for controllable translations. Both qualitative and quantitative results on the CelebA-HQ dataset verify the ability of the proposed HiSD. The code has been released at https://***/imlixinyang/HiSD.

关键词： Training computer vision Codes Scalability Process control Semisupervised learning pattern recognition

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