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Robust 3D Shape Classification via Non-local Graph Attention Network

Robust 3D Shape Classification via Non-local Graph Attention...

引用

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

作者： Qin, Shengwei Li, Zhong Liu, Ligang Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou Peoples R China Huzhou Univ Sch Informat Huzhou Peoples R China Zhejiang Sci Tech Univ Sch Sci Hangzhou Zhejiang Peoples R China Univ Sci & Technol China Sch Math Sci Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

We introduce a non-local graph attention network (NL-GAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In the second sub-network, we enhance the local features with a geometric shape attention map obtained from a global structure network (GSN). To keep rotation invariant and extract more information from sparse point clouds, all sub-networks use the Gram matrices with different dimensions as input for working with robust classification. Additionally, GRN effectively preserves the low-frequency features and improves the classification results. Experimental results on various datasets exhibit that the classification effect of the NLGAT model is better than other state-of-the-art models. Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85.4%) of NLGAT is improved by 39.4% compared with the best development of other methods.

关键词： detection recognition: Categorization retrieval

来源：评论

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Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer

Modernizing Old Photos Using Multiple References via Photore...

引用

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

作者： Gunawan, Agus Kim, Soo Ye Sim, Hyeonjun Lee, Jae-Ho Kim, Munchurl Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Francisco CA USA ETRI Daejeon South Korea Qualcomm San Diego CA USA

ISBN: (纸本)9798350301298

This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via photorealistic style transfer (PST) and further enhances the results to produce modern-looking images. Meanwhile, the synthetic data generation scheme trains the network to effectively utilize multiple references to perform modernization. To evaluate the performance, we propose a new old photos benchmark dataset (CHD) consisting of diverse natural indoor and outdoor scenes. Extensive experiments show that the proposed method outperforms other baselines in performing modernization on real old photos, even though no old photos were used during training. Moreover, our method can appropriately select styles from multiple references for each semantic region in the old photo to further improve the modernization performance.

关键词： Low-level vision

来源：评论

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Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective

Learning Distortion Invariant Representation for Image Resto...

引用

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

作者： Li, Xin Li, Bingchen Jin, Xin Lan, Cuiling Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Eastern Inst Adv Study Ningbo Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations. Our method, termed Distortion Invariant representation Learning (DIL), treats each distortion type and degree as one specific confounder, and learns the distortion-invariant representation by eliminating the harmful confounding effect of each degradation. We derive our DIL with the back-door criterion in causality by modeling the interventions of different distortions from the optimization perspective. Particularly, we introduce counterfactual distortion augmentation to simulate the virtual distortion types and degrees as the confounders. Then, we instantiate the intervention of each distortion with a virtual model updating based on corresponding distorted images, and eliminate them from the meta-learning perspective. Extensive experiments demonstrate the generalization capability of our DIL on unseen distortion types and degrees. Our code will be available at https://***/lixinustc/Causal-IR-DIL.

关键词： Low-level vision

来源：评论

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Learning Sample Relationship for Exposure Correction

Learning Sample Relationship for Exposure Correction

引用

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

作者： Huang, Jie Zhao, Feng Zhou, Man Xiao, Jie Zheng, Naishan Zheng, Kaiwen Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency. In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a mini-batch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design.

关键词： Low-level vision

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ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field

ABLE-NeRF: Attention-Based Rendering with Learnable Embeddin...

引用

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

作者： Tang, Zhe Jun Cham, Tat-Jen Zhao, Haiyu Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Singapore Singapore SenseTime Res Singapore Singapore

ISBN: (纸本)9798350301298

Neural Radiance Field (NeRF) is a popular method in representing 3D scenes by optimising a continuous volumetric scene function. Its large success which lies in applying volumetric rendering (VR) is also its Achilles' heel in producing view-dependent effects. As a consequence, glossy and transparent surfaces often appear murky. A remedy to reduce these artefacts is to constrain this VR equation by excluding volumes with back-facing normal. While this approach has some success in rendering glossy surfaces, translucent objects are still poorly represented. In this paper, we present an alternative to the physics-based VR approach by introducing a self-attention-based framework on volumes along a ray. In addition, inspired by modern game engines which utilise Light Probes to store local lighting passing through the scene, we incorporate Learnable Embeddings to capture view dependent effects within the scene. Our method, which we call ABLE-NeRF, significantly reduces 'blurry' glossy surfaces in rendering and produces realistic translucent surfaces which lack in prior art. In the Blender dataset, ABLE-NeRF achieves SOTA results and surpasses Ref-NeRF in all 3 image quality metrics PSNR, SSIM, LPIPS.

关键词： vision + graphics

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Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution

Equivalent Transformation and Dual Stream Network Constructi...

引用

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

作者： Chao, Jiahao Zhou, Zhou Gao, Hongfan Gong, Jiali Yang, Zhengfeng Zeng, Zhenbing Dehbi, Lydia East China Normal Univ Shanghai Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Beijing Peoples R China

ISBN: (纸本)9798350301298

In recent years, there has been an increasing demand for real-time super-resolution networks on mobile devices. To address this issue, many lightweight super-resolution models have been proposed. However, these models still contain time-consuming components that increase inference latency, limiting their real-world applications on mobile devices. In this paper, we propose a novel model for single-image super-resolution based on Equivalent Transformation and Dual Stream network construction (ETDS). ET method is proposed to transform time-consuming operators into time-friendly operations, such as convolution and ReLU, on mobile devices. Then, a dual stream network is designed to alleviate redundant parameters resulting from the use of ET and enhance the feature extraction ability. Taking full advantage of the advance of ET and the dual stream network structure, we develop the efficient SR model ETDS for mobile devices. The experimental results demonstrate that our ETDS achieves superior inference speed and reconstruction quality compared to previous lightweight SR methods on mobile devices. The code is available at https://***/ECNUSR/ETDS.

关键词： Low-level vision

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StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation using a Pre-t...

引用

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

作者： Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Image and video synthesis and generation vision + graphics vision applications and systems

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6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation

6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose E...

引用

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

作者： Xu, Li Qui, Haoxuan Cai, Yujun Liu, Jun Singapore Univ Technol & Design Singapore Singapore Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350353006

Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds. Meanwhile, diffusion models have shown appealing performance in generating high-quality images from random noise with high indeterminacy through step-by-step denoising. In-spired by their denoising capability, we propose a novel diffusion-based framework (6D-Diff) to handle the noise and indeterminacy in object pose estimation for better performance. In our framework, to establish accurate 2D-3D correspondence, we formulate 2D keypoints detection as a reverse diffusion (denoising) process. To facilitate such a denoising process, we design a Mixture-of-Cauchy-based forward diffusion process and condition the reverse process on the object appearance features. Extensive experiments on the LM-O and YCB-V datasets demonstrate the effectiveness of our framework.

关键词： Object recognition

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Bidirectional Cross-Modal Knowledge Exploration for Video recognition with Pre-trained vision-Language Models

Bidirectional Cross-Modal Knowledge Exploration for Video Re...

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

作者： Wu, Wenhao Wang, Xiaohan Luo, Haipeng Wang, Jingdong Yang, Yi Ouyang, Wanli Univ Sydney Camperdown NSW Australia Baidu Inc Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value of pre-trained VLMs lies in building a bridge between visual and textual domains. In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition. ii) We also present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner, leading to enhanced video representation. Extensive studies on six popular video datasets, including Kinetics-400 & 600, UCF-101, HMDB-51, ActivityNet and Charades, show that our method achieves state-of-the-art performance in various recognition scenarios, such as general, zero-shot, and few-shot video recognition. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model. The code is available at https://***/whwu95/BIKE.

关键词： Video: Action and event understanding

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Zero-shot Referring Image Segmentation with Global-Local Context Features

Zero-shot Referring Image Segmentation with Global-Local Con...

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

作者： Yu, Seonghoon Seo, Paul Hongsuck Son, Jeany GIST AI Grad Sch Gwangju South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-trained cross-modal knowledge from CLIP. In order to obtain segmentation masks grounded to the input text, we propose a mask-guided visual encoder that captures global and local contextual information of an input image. By utilizing instance masks obtained from off-the-shelf mask proposal techniques, our method is able to segment fine-detailed instance-level groundings. We also introduce a global-local text encoder where the global feature captures complex sentence-level semantics of the entire input expression while the local feature focuses on the target noun phrase extracted by a dependency parser. In our experiments, the proposed method outperforms several zero-shot baselines of the task and even the weakly supervised referring expression segmentation method with substantial margins. Our code is available at https://***/Seonghoon-Yu/Zero-shot-RIS.

关键词： language reasoning vision

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