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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods...

引用

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

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin-Alexandru Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan, Sabarinathan Uma, K. Sasithradevi, A. Bama, B. Sathya Roomi, S. Mohamed Mansoor Srivatsav, V Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos, V Feijoo, Daniel Benito, Juan C. Garcia, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S. M. A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad 'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan Univ Elect Sci & Technol China Chengdu Peoples R China Univ Wurzburg Comp Vis Lab Wurzburg Germany Shanghai Jiao Tong Univ Shanghai Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Guangdong Prov Key Lab Fire Sci & Technol Guangzhou 510006 Peoples R China Johns Hopkins Univ Baltimore MD USA Tsinghua Univ Beijing Peoples R China Zhejiang Dahua Technol Co Ltd Hangzhou Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Sch Software Xian Peoples R China Xian Univ Architecture & Technol Sch Comp Sci Xian Peoples R China South China Univ Technol Guangzhou Peoples R China Fuzhou Univ Fuzhou Peoples R China Chengdu Univ Informat Technol Chengdu Peoples R China Hong Kong Univ Sci & Technol Guangzhou Peoples R China Hefei Univ Technol Hefei Peoples R China Shenyang Aerosp Univ Shenyang Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China Imperial Vis Technol Fuzhou Peoples R China Tianjin Univ Tianjin Peoples R China Couger Inc Yokohama Kanagawa Japan Sasi Inst Technol & Engn Tadepalligudem India Vellore Inst Technol Vellore Tamil Nadu India Thiagarajar Coll Engn Madurai India Coventry Univ Coventry W Midlands England Cidaut AI Valladolid Spain Univ Wurzburg CVLab Wurzburg Germany Opt AI Seoul South Korea Univ Teknol Malaysia Malaysia Japan Int Inst Technol MJIIT Kuala Lumpur Malaysia Univ Teknol Malaysia Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur Malaysia KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India KLE Technol Univ Sch Elect & Commun Engn Hubballi Karnataka India KLE Technol Univ Sch Comp Sci & Engn Hubballi Karnataka India Uppsala Univ Uppsala Sweden Northeastern Univ Boston MA USA Bosch Investment Ltd London England Fortinet Inc Sunnyvale CA USA Univ Saskatchewan Saskatoon SK Canada Sard

ISBN: (纸本)9798350365474

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.

关键词： backlighting low light image enhancement non-uniform illumination ultra-high resolution

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Material Palette: Extraction of Materials from a Single Image

Material Palette: Extraction of Materials from a Single Imag...

引用

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

作者： Lopes, Ivan Pizzati, Fabio de Charette, Raoul INRIA Paris France Univ Oxford Oxford England

ISBN: (纸本)9798350353013;9798350353006

Physically-Based Rendering (PBR) is key to modeling the interaction between light and materials, and finds extensive applications across computer graphics domains. However, acquiring PBR materials is costly and requires special apparatus. In this paper, we propose a method to extract PBR materials from a single real-world image. We do so in two steps: first, we map regions of the image to material concept tokens using a diffusion model, allowing the sampling of texture images resembling each material in the scene. Second, we leverage a separate network to decompose the generated textures into spatially varying BRDFs (SVBRDFs), offering us readily usable materials for rendering applications. Our approach relies on existing synthetic material libraries with SVBRDF ground truth. It exploits a diffusion-generated RGB texture dataset to allow generalization to new samples using unsupervised domain adaptation (UDA). Our contributions are thoroughly evaluated on synthetic and real-world datasets. We further demonstrate the applicability of our method for editing 3D scenes with materials estimated from real photographs. Along with video, we share code and models as open-source on the project page: https://***/astra-vision/MaterialPalette

关键词： brdf computer graphics generative ai inversion Material estimation

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A Closer Look at the Few-Shot Adaptation of Large vision-Language Models

A Closer Look at the Few-Shot Adaptation of Large Vision-Lan...

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

作者： Iguez, Julio Silva-Rodr Hajimiri, Sina Ben Ayed, Ismail Dolz, Jose ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350353006

Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made, we reveal that state-of-the-art ETL approaches exhibit strong performance only in narrowly-defined experimental setups, and with a careful adjustment of hyperparameters based on a large corpus of labeled samples. In particular, we make two interesting, and surprising empirical observations. First, to out-perform a simple Linear Probing baseline, these methods require to optimize their hyper-parameters on each target task. And second, they typically underperform -sometimes dramatically-standard zero-shot predictions in the presence of distributional drifts. Motivated by the unrealistic assumptions made in the existing literature, i.e., access to a large validation set and case-specific grid-search for optimal hyperparameters, we propose a novel approach that meets the requirements of real-world scenarios. More concretely, we introduce a CLass-Adaptive linear Probe ( CLAP) objective, whose balancing term is optimized via an adaptation of the general Augmented Lagrangian method tailored to this context. We comprehensively evaluate CLAP on a broad span of datasets and scenarios, demonstrating that it consistently outperforms SoTA approaches, while yet being a much more efficient alternative. Code available at https://***/jusiro/CLAP.

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Pre-trained vision and Language Transformers Are Few-Shot Incremental Learners

Pre-trained Vision and Language Transformers Are Few-Shot In...

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

作者： Park, Keon-Hee Song, Kyungwoo Park, Gyeong-Moon Kyung Hee Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting, and these challenges have driven prior studies to primarily rely on shallow models, such as ResNet-18. Even though their limited capacity can mitigate both forgetting and overfitting issues, it leads to inadequate knowledge transfer during few-shot incremental sessions. In this paper, we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners. To this end, we propose a novel FSCIL framework called PriViLege, Pre-trained vision and Language transformers with prompting functions and knowledge distillation. Our framework effectively addresses the challenges of catastrophic forgetting and overfitting in large models through new pre-trained knowledge tuning (PKT) and two losses: entropy-based divergence loss and semantic knowledge distillation loss. Experimental results show that the proposed PriViLege significantly outperforms the existing state-of-the-art methods with a large margin, e.g., +9.38% in CUB200, +20.58% in CIFAR-100, and +13.36% in miniImageNet. Our implementation code is available at https://***/KHU-AGI/PriViLege.

关键词： Few-shot learning Incremental learning Parameter Efficient Tuning

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ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangli...

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

作者： Jiang, Yankai Huang, Zhongzhen Zhang, Rongzhao Zhang, Xiaofan Zhang, Shaoting Shanghai AI Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China SenseTime Res Shanghai Peoples R China

ISBN: (纸本)9798350353006

The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones, which poses a significant challenge in developing a unified model capable of identifying rare or novel tumor categories not encountered during training. In this paper, we propose a new Zero-shot Pan-Tumor segmentation framework (ZePT) based on query-disentangling and self-prompting to segment unseen tumor categories beyond the training set. ZePT disentangles the object queries into two subsets and trains them in two stages. Initially, it learns a set of fundamental queries for organ segmentation through an object-aware feature grouping strategy, which gathers organ-level visual features. Subsequently, it refines the other set of advanced queries that focus on the auto-generated visual prompts for unseen tumor segmentation. Moreover, we introduce query-knowledge alignment at the feature level to enhance each query's discriminative representation and generalizability. Extensive experiments on various tumor segmentation tasks demonstrate the performance superiority of ZePT, which surpasses the previous counterparts and evidences the promising ability for zero-shot tumor segmentation in real-world settings.

关键词： Medical Image Segmentation vision-Language Model

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TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process

TIGER: Time-Varying Denoising Model for 3D Point Cloud Gener...

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

作者： Ren, Zhiyuan Kim, Minchul Liu, Feng Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9798350353006

Recently, diffusion models have emerged as a new powerful generative method for 3D point cloud generation tasks. However, few works study the effect of the architecture of the diffusion model in the 3D point cloud, resorting to the typical UNet model developed for 2D images. Inspired by the wide adoption of Transformers, we study the complementary role of convolution (from UNet) and attention (from Transformers). We discover that their respective importance change according to the timestep in the diffusion process. At early stage, attention has an out-sized influence because Transformers are found to generate the overall shape more quickly, and at later stages when adding fine detail, convolution starts having a larger impact on the generated point cloud's local surface quality. In light of this observation, we propose a time-varying two-stream denoising model combined with convolution layers and transformer blocks. We generate an optimizable mask from each timestep to reweigh global and local features, obtaining time-varying fused features. Experimentally, we demonstrate that our proposed method quantitatively outperforms other state-of-the-art methods regarding visual quality and diversity. Code is avaiable https://***/Zhiyuan-R/Tiger-Diffusion.

关键词： 3D vision Diffusion Model Generative Model Point Cloud ShapeNet

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SonicvisionLM: Playing Sound with vision Language Models

SonicVisionLM: Playing Sound with Vision Language Models

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

作者： Xie, Zhifeng Yu, Shengye He, Qile Li, Mengtian Shanghai Univ Shanghai Peoples R China Shanghai Engn Res Ctr Mot Picture Special Effects Shanghai Peoples R China

ISBN: (纸本)9798350353006

There has been a growing interest in the task of generating sound for silent videos, primarily because of its practicality in streamlining video post-production. However, existing methods for video-sound generation attempt to directly create sound from visual representations, which can be challenging due to the difficulty of aligning visual representations with audio representations. In this paper, we present SonicvisionLM, a novel framework aimed at generating a wide range of sound effects by leveraging vision-language models(VLMs). Instead of generating audio directly from video, we use the capabilities of powerful VLMs. When provided with a silent video, our approach first identifies events within the video using a VLM to suggest possible sounds that match the video content. This shift in approach transforms the challenging task of aligning image and audio into more well-studied sub-problems of aligning image-to-text and text-to-audio through the popular diffusion models. To improve the quality of audio recommendations with LLMs, we have collected an extensive dataset that maps text descriptions to specific sound effects and developed a time-controlled audio adapter. Our approach surpasses current state-of-the-art methods for converting video to audio, enhancing synchronization with the visuals, and improving alignment between audio and video components. Project page: https://***. io/***/

关键词： audio generation multi-modal vision-language model

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Instance-Aware Group Quantization for vision Transformers

Instance-Aware Group Quantization for Vision Transformers

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

作者： Jaehyeon, Moon Kim, Dohyung Cheon, Junyong Ham, Bumsub Yonsei Univ Seoul South Korea Articron Bogota Colombia

ISBN: (纸本)9798350353006

Post-training quantization (PTQ) is an efficient model compression technique that quantizes a pretrained full-precision model using only a small calibration set of unlabeled samples without retraining. PTQ methods for convolutional neural networks (CNNs) provide quantization results comparable to full-precision counterparts. Directly applying them to vision transformers (ViTs), however, incurs severe performance degradation, mainly due to the differences in architectures between CNNs and ViTs. In particular, the distribution of activations for each channel vary drastically according to input instances, making PTQ methods for CNNs inappropriate for ViTs. To address this, we introduce instance-aware group quantization for ViTs (IGQ-ViT). To this end, we propose to split the channels of activation maps into multiple groups dynamically for each input instance, such that activations within each group share similar statistical properties. We also extend our scheme to quantize softmax attentions across tokens. In addition, the number of groups for each layer is adjusted to minimize the discrepancies between predictions from quantized and full-precision models, under a bit-operation (BOP) constraint. We show extensive experimental results on image classification, object detection, and instance segmentation, with various transformer architectures, demonstrating the effectiveness of our approach.

关键词： Post-training quantization ViT

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Any-Shift Prompting for Generalization over Distributions

Any-Shift Prompting for Generalization over Distributions

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

作者： Xiao, Zehao Shen, Jiayi Derakhshani, Mohammad Mandi Liao, Shengcai Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Core42 Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350353006

Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless, conventional prompt learning methods overfit their training distribution and lose the generalization ability on test distributions. To improve generalization across various distribution shifts, we propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. We explicitly connect training and test distributions in the latent space by constructing training and test prompts in a hierarchical architecture. Within this frame-work, the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. To effectively encode the distribution information and their relationships, we further introduce a transformer inference network with a pseudo-shift training mechanism. The network generates the tailored test prompt with both training and test information in a feedforward pass, avoiding extra training costs at test time. Extensive experiments on twenty-three datasets demonstrate the effectiveness of any-shift prompting on the generalization over various distribution shifts.

关键词： distribution shifts out-of-distribution generalization prompt learning vision-language

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The Audio-Visual Conversational Graph: From an Egocentric-Exocentric Perspective

The Audio-Visual Conversational Graph: From an Egocentric-Ex...

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

作者： Jia, Wenqi Liu, Miao Jiang, Hao Ananthabhotla, Ishwarya Rehg, James M. Ithapu, Vamsi Krishna Gao, Ruohan Georgia Tech Atlanta GA 30332 USA Meta Real Labs Menlo Pk CA 94025 USA UIUC Champaign IL USA Meta GenAI Menlo Pk CA USA

ISBN: (纸本)9798350353006

In recent years, the thriving development of research related to egocentric videos has provided a unique perspective for the study of conversational interactions, where both visual and audio signals play a crucial role. While most prior work focus on learning about behaviors that directly involve the camera wearer, we introduce the Ego-Exocentric Conversational Graph Prediction problem, marking the first attempt to infer exocentric conversational interactions from egocentric videos. We propose a unified multi-modal framework-Audio-Visual Conversational Attention (AV-CONV), for the joint prediction of conversation behaviors-speaking and listening-for both the camera wearer as well as all other social partners present in the egocentric video. Specifically, we adopt the self-attention mechanism to model the representations across-time, across-subjects, and across-modalities. To validate our method, we conduct experiments on a challenging egocentric video dataset that includes multi-speaker and multi-conversation scenarios. Our results demonstrate the superior performance of our method compared to a series of baselines. We also present detailed ablation studies to assess the contribution of each component in our model. Check our Project Page.

关键词： egocentric vision Multi-modal learning social ai

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