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PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image

PREIM3D: 3D Consistent Precise Image Attribute Editing from ...

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conference on computer vision and pattern recognition (CVPR)

作者： Jianhui Li Jianmin Li Haoji Zhang Shilong Liu Zhengyi Wang Zihao Xiao Kaiwen Zheng Jun Zhu Department of Computer Science and Technology Institute for AI BNRist Tsinghua University State Key Laboratory of Astronautic dynamics Xi'an Satellite Control Center RealAI

We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map images into a 3D generator's latent space or by per-image latent code optimization and then edited images in the latent space. Despite their promising results near the input view, they still suffer from the 3D inconsistency of produced images at large camera poses and imprecise image attribute editing, like affecting unspecified attributes during editing. For more efficient image inversion, we train a shared encoder for all images. To alleviate 3D inconsistency at large camera poses, we propose two novel methods, an alternating training scheme and a multi-view identity loss, to maintain 3D consistency and subject identity. As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images. We compare the latent space and inversion manifold of GAN models and demonstrate that editing in the inversion manifold can achieve better results in both quantitative and qualitative evaluations. Extensive experiments show that our method produces more 3D consistent images and achieves more precise image editing than previous work. Source code and pretrained models can be found on our project page: https://***/Preim3D/.

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PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection

PiMAE: Point Cloud and Image Interactive Masked Autoencoders...

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conference on computer vision and pattern recognition (CVPR)

作者： Anthony Chen Kevin Zhang Renrui Zhang Zihan Wang Yuheng Lu Yandong Guo Shanghang Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University The Chinese University of Hong Kong Wukong Lab iKingtec Beijing University of Posts and Telecommunications

Masked Autoencoders learn strong visual representations and achieve state-of-the-art results in several independent modalities, yet very few works have addressed their capabilities in multi-modality settings. In this work, we focus on point cloud and RGB image data, two modalities that are often presented together in the real world, and explore their meaningful interactions. To improve upon the cross-modal synergy in existing works, we propose Pi-MAE, a self-supervised pre-training framework that promotes 3D and 2D interaction through three aspects. Specifically, we first notice the importance of masking strategies between the two sources and utilize a projection module to complementarily align the mask and visible tokens of the two modalities. Then, we utilize a well-crafted two-branch MAE pipeline with a novel shared decoder to promote cross-modality interaction in the mask tokens. Finally, we design a unique cross-modal reconstruction module to enhance representation learning for both modalities. Through extensive experiments performed on large-scale RGB-D scene understanding benchmarks (SUN RGB-D and ScannetV2), we discover it is nontrivial to interactively learn point-image features, where we greatly improve multiple 3D detectors, 2D detectors, and few-shot classifiers by 2.9%, 6.7%, and 2.4%, respectively. Code is available at https://***/BLVLab/PiMAE.

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Divide and Adapt: Active Domain Adaptation via Customized Learning

Divide and Adapt: Active Domain Adaptation via Customized Le...

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conference on computer vision and pattern recognition (CVPR)

作者： Duojun Huang Jichang Li Weikai Chen Junshi Huang Zhenhua Chai Guanbin Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Research Institute Sun Yat-sen University Shenzhen China The University of Hong Kong Tencent America Meituan

Active domain adaptation (ADA) aims to improve the model adaptation performance by incorporating active learning (AL) techniques to label a maximally-informative subset of target samples. Conventional AL methods do not consider the existence of domain shift, and hence, fail to identify the truly valuable samples in the context of domain adaptation. To accommodate active learning and domain adaption, the two naturally different tasks, in a collaborative framework, we advocate that a customized learning strategy for the target data is the key to the success of ADA solutions. We present Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target instances into four categories with stratified transferable properties. With a novel data subdivision protocol based on uncertainty and domainness, DiaNA can accurately recognize the most gainful samples. While sending the informative instances for annotation, DiaNA employs tailored learning strategies for the remaining categories. Furthermore, we propose an informativeness score that unifies the data partitioning criteria. This enables the use of a Gaussian mixture model (GMM) to automatically sample unlabeled data into the proposed four categories. Thanks to the “divide-and-adapt” spirit, DiaNA can handle data with large variations of domain gap. In addition, we show that DiaNA can generalize to different domain adaptation settings, such as unsupervised domain adaptation (UDA), semi-supervised domain adaptation (SSDA), source-free domain adaptation (SFDA), etc.

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OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields

OReX: Object Reconstruction from Planar Cross-sections Using...

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conference on computer vision and pattern recognition (CVPR)

作者： Haim Sawdayee Amir Vaxman Amit H. Bermano The Blavatnik School of Computer Science Tel Aviv University School of Informatics The University of Edinburgh

Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing this sparse and ill-posed problem either produce low quality results, or rely on additional priors such as target topology, appearance information, or input normal directions. In this paper, we present OReX, a method for 3D shape reconstruction from slices alone, featuring a Neural Field as the interpolation prior. A modest neural network is trained on the input planes to return an inside/outside estimate for a given 3D coordinate, yielding a powerful prior that induces smoothness and self-similarities. The main challenge for this approach is high-frequency details, as the neural prior is overly smoothing. To alleviate this, we offer an iterative estimation architecture and a hierarchical input sampling scheme that encourage coarse-to-fine training, allowing the training process to focus on high frequencies at later stages. In addition, we identify and analyze a ripple-like effect stemming from the mesh extraction step. We mitigate it by regularizing the spatial gradients of the indicator function around input in/out boundaries during network training, tackling the problem at the root. Through extensive qualitative and quantitative experimentation, we demonstrate our method is robust, accurate, and scales well with the size of the input. We report state-of-the-art results compared to previous approaches and recent potential solutions, and demonstrate the benefit of our individual contributions through analysis and ablation studies. 1 1 Code and data available at https://***/haimsaw/OReX

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Probing Sentiment-Oriented PreTraining Inspired by Human Sentiment Perception Mechanism

Probing Sentiment-Oriented PreTraining Inspired by Human Sen...

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conference on computer vision and pattern recognition (CVPR)

作者： Tinglei Feng Jiaxuan Liu Jufeng Yang TMCC College of Computer Science Nankai University China

Pre-training of deep convolutional neural networks (DC-NNs) plays a crucial role in the field of visual sentiment analysis (VSA). Most proposed methods employ the off-the-shelf backbones pre-trained on large-scale object classification datasets (i.e., ImageNet). While it boosts performance for a big margin against initializing model states from random, we argue that DCNNs simply pre-trained on ImageNet may excessively focus on recognizing objects, but failed to provide high-level concepts in terms of sentiment. To address this long-term overlooked problem, we propose a sentiment-oriented pre-training method that is built upon human visual sentiment perception (VSP) mechanism. Specifically, we factorize the process of VSP into three steps, namely stimuli taking, holistic organizing, and high-level perceiving. From imitating each VSP step, a total of three models are separately pre-trained via our devised sentiment-aware tasks that contribute to excavating sentiment-discriminated representations. Moreover, along with our elaborated multi-model amalgamation strategy, the prior knowledge learned from each perception step can be effectively transferred into a single target model, yielding substantial performance gains. Finally, we verify the superiorities of our proposed method over extensive experiments, covering mainstream VSA tasks from single-label learning (SLL), multi-label learning (MLL), to label distribution learning (LDL). Experiment results demonstrate that our proposed method leads to unanimous improvements in these downstream tasks. Our code is released on https://***/tinglyfeng/sentiment_pretraining.

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TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization

TWINS: A Fine-Tuning Framework for Improved Transferability ...

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conference on computer vision and pattern recognition (CVPR)

作者： Ziquan Liu Yi Xu Xiangyang Ji Antoni B. Chan Department of Computer Science City University of Hong Kong School of Artificial Intelligence Dalian University of Technology Department of Automation Tsinghua University

Recent years have seen the ever-increasing importance of pre-trained models and their downstream training in deep learning research and applications. At the same time, the defense for adversarial examples has been mainly inves-tigated in the context of training from random initialization on simple classification tasks. To better exploit the potential of pre-trained models in adversarial robustness, this paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. Existing research has shown that since the robust pre-trained model has already learned a robust feature extractor, the crucial question is how to maintain the robustness in the pre-trained model when learning the downstream task. We study the model-based and data-based approaches for this goal and find that the two common approaches cannot achieve the objective of improving both generalization and adversarial robustness. Thus, we propose a novel statistics-based approach, Two-WIng NormliSation (TWINS)fine-tuning framework, which consists of two neural networks where one of them keeps the population means and variances of pre-training data in the batch normalization layers. Besides the robust information transfer, TWINS increases the effective learning rate without hurting the training stability since the relationship between a weight norm and its gradient norm in standard batch normalization layer is broken, resulting in a faster es-cape from the sub-optimal initialization and alleviating the robust overfitting. Finally, TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.

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Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Learning Spatial-Temporal Implicit Neural Representations fo...

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conference on computer vision and pattern recognition (CVPR)

作者： Yunfan Lu Zipeng Wang Minjie Liu Hongjian Wang Lin Wang AI Thrust HKUST(GZ) Shenzhen International Graduate School Tsinghua University Dept. of Computer Science and Engineering HKUST

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video super-resolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https://***/cvpr23/egvsr.

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Density-Insensitive Unsupervised Domain Adaption on 3D Object Detection

Density-Insensitive Unsupervised Domain Adaption on 3D Objec...

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conference on computer vision and pattern recognition (CVPR)

作者： Qianjiang Hu Daizong Liu Wei Hu Wangxuan Institute of Computer Technology Peking University Beijing China

3D object detection from point clouds is crucial in safety-critical autonomous driving. Although many works have made great efforts and achieved significant progress on this task, most of them suffer from expensive annotation cost and poor transferability to unknown data due to the domain gap. Recently, few works attempt to tackle the domain gap in objects, but still fail to adapt to the gap of varying beam-densities between two domains, which is critical to mitigate the characteristic differences of the LiDAR collectors. To this end, we make the attempt to propose a density-insensitive domain adaption framework to address the density-induced domain gap. In particular, we first introduce Random Beam Re-Sampling (RBRS) to enhance the robustness of 3D detectors trained on the source domain to the varying beam-density. Then, we take this pre-trained detector as the backbone model, and feed the unlabeled target domain data into our newly designed task-specific teacher-student framework for predicting its high-quality pseudo labels. To further adapt the property of density-insensitivity into the target domain, we feed the teacher and student branches with the same sample of different densities, and propose an Object Graph Alignment (OGA) module to construct two object-graphs between the two branches for enforcing the consistency in both the attribute and relation of cross-density objects. Experimental results on three widely adopted 3D object detection datasets demonstrate that our proposed domain adaption method outperforms the state-of-the-art methods, especially over varying-density data. Code is available at https://***/WoodwindHu/DTS.

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NTIRE 2024 Challenge on Light Field Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Light Field Image Super-Resolution: ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yingqian Wang Zhengyu Liang Qianyu Chen Longguang Wang Jungang Yang Radu Timofte Yulan Guo Wentao Chao Yiming Kan Xuechun Wang Fuqing Duan Guanghui Wang Wang Xia Ziqi Wang Yue Yan Peiqi Xia Shunzhou Wang Yao Lu Angulia Yang Kai Jin Zeqiang Wei Sha Guo Mingzhi Gao Xiuzhuang Zhou Zhongxin Yu Shaofei Luo Cheng Zhong Shaorui Chen Long Peng Yuhong He Gaosheng Liu Huanjing Yue Jingyu Yang Zhengjian Yao Jiakui Hu Lujia Jin Zhi-Song Liu Chenhang He Jun Xiao Xiuyuan Wang Zonglin Tian Yifan Mao Deyang Liu Shizheng Li Ping An

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this report, we summarize the 2nd NTIRE challenge on light field (LF) image super-resolution (SR) with a focus on new methods and results. This challenge aims at superresolving LF images under the standard bicubic downsampling degradation with a magnification factor of ×4. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. This year of challenge has two tracks, including one track on fidelity (i.e., restoration accuracy in terms of PSNR) only, and the other track on fidelity with an extra constraint on model size and computational cost. In total, 125 participants were successfully registered for this challenge, and 9 teams have successfully submitted results with PSNR scores higher than the baseline methods. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR.

关键词： Degradation computer vision conferences Computational modeling Superresolution Market research Light fields

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Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos

Efficient Semantic Segmentation by Altering Resolutions for ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yubin Hu Yuze He Yanghao Li Jisheng Li Yuxing Han Jiangtao Wen Yong-Jin Liu Department of Computer Science and Technology BNRist Tsinghua University Shenzhen International Graduate School Tsinghua University Eastern Institute for Advanced Study

Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame prediction for videos of high frame rates. In recent work, compact models or adaptive network strategies have been proposed for efficient VSS. However, they did not consider a crucial factor that affects the computational cost from the input side: the input resolution. In this paper, we propose an altering resolution framework called AR-Seg for compressed videos to achieve efficient VSS. AR-Seg aims to reduce the computational cost by using low resolution for non-keyframes. To prevent the performance degradation caused by downsampling, we design a Cross Resolution Feature Fusion (CR-eFF) module, and supervise it with a novel Feature Similarity Training (FST) strategy. Specifically, CReFF first makes use of motion vectors stored in a compressed video to warp features from high-resolution keyframes to low-resolution non-keyframes for better spatial alignment, and then selectively aggregates the warped features with local attention mechanism. Furthermore, the proposed FST supervises the aggregated features with high-resolution features through an explicit similarity loss and an implicit constraint from the shared decoding layer. Extensive experiments on CamVid and Cityscapes show that AR-Seg achieves state-of-the-art performance and is compatible with different segmentation backbones. On CamVid, AR-Seg saves 67% computational cost (measured in GFLOPs) with the PSPNet18 back-bone while maintaining high segmentation accuracy. Code: https://***/THU-LYJ-Lab/AR-Seg.

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