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

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NTIRE 2024 Challenge on Bracketing Image Restoration and Enhancement: Datasets, Methods and Results

NTIRE 2024 Challenge on Bracketing Image Restoration and Enh...

引用

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

作者： Zhilu Zhang Shuohao Zhang Renlong Wu Wangmeng Zuo Radu Timofte Xiaoxia Xing Hyunhee Park Sejun Song Changho Kim Xiangyu Kong Jinlong Wu Jianxing Zhang Jingfan Tan Zikun Liu Wenhan Luo Wenjie Lin Chengzhi Jiang Mingyan Han Zhen Liu Ting Jiang Jinting Luo Shen Cheng Linze Li Xinhan Niu Shuaicheng Liu Kexin Dai Kangzhen Yang Tao Hu Xiangyu Chen Yu Cao Qingsen Yan Yanning Zhang Genggeng Chen Yongqing Yang Wei Dong Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Peng Zhang Yifei Chen Wenbo Xiong Zhijun Song Pu Cheng Taolue Feng Yunqing He Daiguo Zhou Ying Huang Xiaowen Ma Peng Wu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-light photography presents significant challenges. Multi-image processing methods have made numerous attempts to obtain high-quality photos, yet remain unsatisfactory. Recently, bracketing image restoration and enhancement has received increased attention. By leveraging the full potential of multi-exposure images, several tasks (including denoising, deblurring, high dynamic range enhancement, and super-resolution) can be jointly addressed. This paper reviews the NTIRE 2024 challenge on bracketing image restoration and enhancement. In the challenge, participants are required to process multi-exposure RAW images to generate noise-free, blur-free, high dynamic range, and even higher-resolution RAW images. The challenge comprises two tracks. Track 1 does not incorporate the super-resolution task, whereas Track 2 does. Each track featured five teams participating in the final testing phase. The proposed methods establish new state-of-the-art performance benchmarks.

关键词： Photography computer vision Reviews conferences Superresolution Noise reduction Benchmark testing

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CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation

CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D F...

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

作者： Rahul Dey Bernhard Egger Vishnu Naresh Boddeti Ye Wang Tim K. Marks Mitsubishi Electric Research Laboratories (MERL) Michigan State University (MSU) Friedrich-Alexander-Universitat (FAU) Erlangen-Nürnberg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating 3D faces and rendering them to images has numerous practical applications in areas including AR/VR, dataset generation, and avatar creation. In recent years, there has been a significant surge in the development of high-fidelity 3D face generation techniques such as StyleSDF, which combine the benefits of 3D implicit neural representations with those of style-based 2D generative adversarial networks (GANs). Although these implicit 3D GAN approaches generate highly realistic faces using a 3D representation, the properties of the generated faces cannot easily be edited or controlled. Meanwhile, linear 3D morphable models (3DMMs) and their nonlinear extensions have also made significant strides in their expressive capacity and quality, but they have yet to match the image quality achieved by GANs. This paper proposes a new method, CoLa-SDF, which combines the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by the impressive photorealism and expressive 3D representations of StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN. We demonstrate high-fidelity image synthesis and subsequent 3D manipulation with full control over the disentangled latent parameters.

关键词： Image quality Solid modeling Photorealism Three-dimensional displays Image synthesis Generative adversarial networks Rendering (computer graphics)

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Wake-Sleep Energy Based Models for Continual Learning

Wake-Sleep Energy Based Models for Continual Learning

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

作者： Vaibhav Singh Anna Choromanska Shuang Li Yilun Du Concordia University Mila New York University University of Toronto Massachusetts Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel approach for continually training Energy-Based Models (EBMs) on the classification problems in the challenging setting of class incremental learning. Despite the fact that EBMs offer longer retention of knowledge on prior tasks, training EBMs contrastively remains a challenge. Driven by biological plausibility, we leverage the observation that sleep in humans supports active system consolidation and propose a new approach for training EBMs, which we call Wake-Sleep Energy Based Models (WS-EBMs), which rely on wake-sleep cycles. Our training approach consists of short wake phases followed by long sleep phases. During the short wake phase, the free energy associated with ground truth labels is minimized, which conditions the model towards the correct solutions. This is followed by a long sleep phase, where the free energy of the whole system is minimized contrastively, which allows the model to push the energy of incorrect solutions further from the correct response. We provide a theoretical analysis of WS-EBM showing that it satisfies the sufficient condition for designing proper EBM loss. Our empirical evaluation confirms the plausibility of our approach and demonstrates favorable performance of WS-EBM compared to traditional EBM training as well as state-of-the-art class-incremental continual learning techniques. Furthermore, our proposed two-phase training strategy can be easily integrated with existing techniques resulting in substantial boosts in their performance. Finally, we also provide interesting insights justifying our approach by analyzing the orthogonality between the sequential task vectors, and flatness of the optimized energy surfaces, which may guide the design of class incremental continual learning strategies.

关键词： Training Continuing education Sufficient conditions computer vision Incremental learning Biological system modeling conferences

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DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM

DTLLM-VLT: Diverse Text Generation for Visual Language Track...

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

作者： Xuchen Li Xiaokun Feng Shiyu Hu Meiqi Wu Dailing Zhang Jing Zhang Kaiqi Huang CRISE Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual Language Tracking (VLT) enhances single object tracking (SOT) by integrating natural language descriptions from a video, for the precise tracking of a specified object. By leveraging high-level semantic information, VLT guides object tracking, alleviating the constraints associated with relying on a visual modality. Nevertheless, most VLT benchmarks are annotated in a single granularity and lack a coherent semantic framework to provide scientific guidance. Moreover, coordinating human annotators for high-quality annotations is laborious and time-consuming. To address these challenges, we introduce DTLLM-VLT, which automatically generates extensive and multi-granularity text to enhance environmental diversity. (1) DTLLM-VLT generates scientific and multi-granularity text descriptions using a cohesive prompt framework. Its succinct and highly adaptable design allows seamless integration into various visual tracking benchmarks. (2) We select three prominent benchmarks to deploy our approach: short-term tracking, long-term tracking, and global instance tracking. We offer four granularity combinations for these benchmarks, considering the extent and density of semantic information, thereby showcasing the practicality and versatility of DTLLM-VLT. (3) We conduct comparative experiments on VLT benchmarks with different text granularities, evaluating and analyzing the impact of diverse text on tracking performance. Conclusionally, this work leverages LLM to provide multi-granularity semantic information for VLT task from efficient and diverse perspectives, enabling fine-grained evaluation of multi-modal trackers. In the future, we believe this work can be extended to more datasets to support vision datasets understanding.

关键词： Visualization computer vision Annotations conferences Semantics Natural languages Benchmark testing

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Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using Multiparametric MRI

Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using...

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

作者： Ekaterina Redekop Mara Pleasure Zichen Wang Karthik V Sarma Adam Kinnaird William Speier Corey W Arnold University of California Los Angeles USA University of Alberta Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multiparametric magnetic resonance imaging (mpMRI) plays an essential role in prostate cancer diagnosis as it can noninvasively localize and grade lesions based on their suspicion of representing clinically significant prostate cancer (csPCa). With the development of deep learning, automatic solutions for csPCa detection based on mpMRI have been developed; however, mpMRI data introduces several difficulties, including data scarcity, heterogeneity in image quality across institutions, and missing modalities. This work addresses these difficulties by building a radiology-based foundational model for prostate cancer mpMRI. Foundation models are deep learning models pretrained on a large-scale dataset and they have recently gained significant interest in computer vision and natural language applications. After pretraining, these models are often adapted for a variety of downstream tasks using smaller datasets from within the same domain. In this work, a large prostate multiparametric MRI (mpMRI) dataset was collected by combining data from our institution with two publicly available datasets. Joint modeling of all mpMRI modalities is essential for accurate prostate cancer diagnosis; however, some of these modalities may be missing. Using unsupervised learning, we pretrained modality-specific vector quantized variational autoencoders (VQ-VAE) to form a radiology foundational model. The learned codebook from VQ-VAE was then used to train a multimodal transformer to perform the diagnosis of clinically significant prostate cancer (csPCa). The proposed multimodal transformer models long-range dependencies between latent representations of input modalities and is augmented with modality-level dropout to increase the model robustness to incomplete modalities. Our framework outperforms previously published work and achieves an average AUC/sensitivity/specificity of 0.764/0.690/0.781. Our results show that pretraining on a larger dataset in combination with the power of transfor

关键词： Deep learning Adaptation models computer vision Frequency modulation Computational modeling Magnetic resonance imaging Radiology

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NTIRE 2021 Depth Guided Image Relighting Challenge

NTIRE 2021 Depth Guided Image Relighting Challenge

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

作者： El Helou, Majed Zhou, Ruofan Susstrunk, Sabine Timofte, Radu Suin, Maitreya Rajagopalan, A. N. Wang, Yuanzhi Lu, Tao Zhang, Yanduo Wu, Yuntao Yang, Hao-Hsiang Chen, Wei-Ting Kuo, Sy-Yen Luo, Hao-Lun Zhang, Zhiguang Luo, Zhipeng He, Jianye Zhu, Zuo-Liang Li, Zhen Qiu, Jia-Xiong Kuang, Zeng-Sheng Lu, Cheng-Ze Cheng, Ming-Ming Shao, Xiu-Li Li, Chenghua Ding, Bosong Qian, Wanli Li, Fangya Li, Fu Deng, Ruifeng Lin, Tianwei Liu, Songhua Li, Xin He, Dongliang Yazdani, Amirsaeed Guo, Tiantong Monga, Vishal Nsampi, Ntumba Elie Hu, Zhongyun Wang, Qing Nathan, Sabari Kansal, Priya Zhao, Tongtong Zhao, Shanshan Ecole Polytech Fed Lausanne Lausanne Switzerland Swiss Fed Inst Technol Zurich Switzerland ASUSTeK Comp Inc ASUS Intelligent Cloud Serv Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Chinese Acad Sci Inst Automat Beijing Peoples R China Couger Inc Tokyo Japan DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Penn State Univ Sch Elect Engn & Comp Sci Informat Proc & Algorithms Lab iPAL University Pk PA 16802 USA Indian Inst Technol Madras Chennai Tamil Nadu India Nankai Univ Tianjin Peoples R China Northwestern Polytech Univ Sch Comp Sci Comp Vis & Computat Photog Grp Xian Peoples R China Dalian Maritime Univ Dalian Peoples R China China Everbright Bank Shanghai Peoples R China Baidu Inc Dept Comp Vis Vis Beijing Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan Peoples R China

ISBN: (纸本)9781665448994

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper.

关键词： Geometry computer vision Target tracking Image color analysis conferences Lighting Transforms

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ART•V: Auto-Regressive Text-to-Video Generation with Diffusion Models

ART•V: Auto-Regressive Text-to-Video Generation with Diffus...

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

作者： Wenming Weng Ruoyu Feng Yanhui Wang Qi Dai Chunyu Wang Dacheng Yin Zhiyuan Zhao Kai Qiu Jianmin Bao Yuhui Yuan Chong Luo Yueyi Zhang Zhiwei Xiong University of Science and Technology of China Microsoft Research Asia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present ART•V, an efficient framework for autoregressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-shot, ART•V generates a single frame at a time, conditioned on the previous ones. The framework offers three distinct advantages. First, it only learns simple continual motions between adjacent frames, therefore avoiding modeling complex long-range motions that require huge training data. Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications. Third, it can generate arbitrarily long videos conditioned on a variety of prompts such as text, image or their combinations, making it highly versatile and flexible. To combat the common drifting issue in AR models, we propose masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting. Moreover, we further enhance generation coherence by conditioning it on the initial frame, which typically contains minimal noise. This is particularly useful for long video generation. When trained for only two weeks on four GPUs, ART•V already can generate videos with natural motions, rich details and a high level of aesthetic quality. Besides, it enables various appealing applications, e.g. composing a long video from multiple text prompts.

关键词： computer vision conferences Noise Training data Coherence Predictive models Diffusion models

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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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RLNet: Robust Linearized Networks for Efficient Private Inference

RLNet: Robust Linearized Networks for Efficient Private Infe...

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

作者： Sreetama Sarkar Souvik Kundu Peter A. Beereil Universiy of Southern California Los Angeles USA Intel Labs San Diego USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The growing concern about data privacy has led to the development of private inference (PI) frameworks in client-server applications which protects both data privacy and model IP. However, the cryptographic primitives required yield significant latency overheads which limits their wide-spread application. At the same time, changing environments demand PI services to be robust against various naturally occurring and gradient-based perturbations. Despite several works focused on the development of latency-efficient models suitable for PI, the impact of these models on robustness has remained unexplored. Towards this goal, this paper presents RLNet, a class of models that can yield latency improvement via the reduction of high-latency ReLU operations while improving the model performance on both clean and corrupted images. In particular, RLNet models provide a "triple win ticket" of improved classification accuracy on clean, naturally perturbed, and gradient- based perturbed images using a shared-mask shared-weight architecture with over an order of magnitude fewer ReLUs than baseline models. To demonstrate the efficacy of RLNet, we perform extensive experiments with ResNet and WRN model variants on CIFAR-10, CIFAR-100, and Tiny- ImageNet datasets. Our experimental evaluations show that RLNet can yield models with up to 11.14× fewer ReLUs, with accuracy close to the all-ReLU models, on clean, naturally perturbed, and gradient-based perturbed images. Compared with the SoTA non-robust linearized models at similar ReLU budgets, RLNet achieves an improvement in adversarial accuracy of up to ~47%, in naturally perturbed accuracy of up to ~16.4%, while improving clean image accuracy up to ~1.5%. Code is available at: https://***/sreetamasarkar/rlnet.

关键词： Data privacy computer vision Accuracy Computational modeling Perturbation methods Transformers Robustness

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T2LM: Long-Term 3D Human Motion Generation from Multiple Sentences

T2LM: Long-Term 3D Human Motion Generation from Multiple Sen...

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

作者： Taeryung Lee Fabien Baradel Thomas Lucas Kyoung Mu Lee Grégory Rogez IPAI & ASRI Naver LABS Europe Dept. of ECE Seoul National University Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a stream of multiple sentences (i.e., paragraph). Previous long-term motion generating approaches were mostly based on recurrent methods, using previously generated motion chunks as input for the next step. However, this approach has two drawbacks: 1) it relies on sequential datasets, which are expensive; 2) these methods yield unrealistic gaps between motions generated at each step. To address these issues, we introduce simple yet effective T2LM, a continuous long-term generation framework that can be trained without sequential data. T2LM comprises two components: a 1D-convolutional VQVAE, trained to compress motion to sequences of latent vectors, and a Transformer-based Text Encoder that predicts a latent sequence given an input text. At inference, a sequence of sentences is translated into a continuous stream of latent vectors. This is then decoded into a motion by the VQVAE decoder; the use of 1D convolutions with a local temporal receptive field avoids temporal inconsistencies between training and generated sequences. This simple constraint on the VQ-VAE allows it to be trained with short sequences only and produces smoother transitions. T2LM outperforms prior long-term generation models while overcoming the constraint of requiring sequential data; it is also competitive with SOTA single-action generation models.

关键词： Training computer vision Three-dimensional displays conferences Computational modeling Transformers Vectors

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