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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 ieee.

关键词： Demulsification

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MARRS: Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation

MARRS: Modern Backbones Assisted Co-training for Rapid and R...

引用

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

作者： Saurabh Kumar Jain Sukhendu Das Department of Computer Science Engineering Visualization and Perception Lab Indian Institute of Technology Madras India

Semi-Supervised Domain Adaptation (SSDA) aims to develop domain invariant models from scarcely labeled target domain in addition to the fully labeled source domain. Current SSDA works are often applied in conjunction with ResNet34 backbone, which makes them overlook the advantages of utilizing other backbones. Hence, in this paper, we investigate the impact of employing different modern backbones in SSDA and propose a novel solution named Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation (MARRS), that uses discriminative features of two modern backbones for training linear classifiers using the well established co-training framework. To induce diversity among classifiers for effective co-training, we propose a novel module that produces diversity at three levels, namely image, backbone, and feature distribution levels. Experiments reveal that MARRS not only achieves state-of-the-art performance across all popular SSDA datasets, but also drastically cuts the computation time compared to other SSDA approaches, making MARRS a rapid and robust solution for SSDA. We also provide extensive ablation experiments to verify our framework’s vitality and primary design choices.

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Dataset condensation with latent quantile matching

Dataset condensation with latent quantile matching

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

作者： Wei Wei Tom De Schepper Kevin Mets Department of Computer Science IDLab University of Antwerp - imec Antwerp Belgium IDLab Faculty of Applied Engineering University of Antwerp - imec Antwerp Belgium

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dataset condensation (DC) methods aim to learn a smaller, synthesized dataset with informative data records to accelerate the training of machine learning models. Current distribution matching (DM) based DC methods learn a synthesized dataset by matching the mean of the latent embeddings between the synthetic and the real dataset. However, two distributions with the same mean can still be vastly different. In this work, we demonstrate the shortcomings of using Maximum Mean Discrepancy to match latent distributions, i.e., the weak matching power and lack of outlier regularization. To alleviate these shortcomings, we propose our new method: Latent Quantile Matching (LQM), which matches the quantiles of the latent embeddings to minimize the goodness of fit test statistic between two distributions. Empirical experiments on both image and graph-structured datasets show that LQM matches or outperforms previous state of the art in distribution matching based DC. Moreover, we show that LQM improves the performance in continual graph learning (CGL) setting, where memory efficiency and privacy can be important. Our work sheds light on the application of DM based DC for CGL.

关键词： Training Measurement Data privacy conferences Memory management Machine learning Current distribution

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Efficient local correlation volume for unsupervised optical flow estimation on small moving objects in large satellite images

Efficient local correlation volume for unsupervised optical ...

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

作者： Sarra Khairi Etienne Meunier Renaud Fraisse Patrick Bouthemy Inria Rennes France Airbus Defence & Space Toulouse France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With the advent of deep learning methods, performance and efficiency of optical flow estimation has significantly increased, especially for supervised models. However, they do not generalize well to more specific data involving small moving objects in large images, such as high-resolution aerial or satellite sequences. In addition, annotation and realistic simulation are difficult for these contents, which calls for unsupervised alternatives. Yet, the latter are still less accurate than their supervised counterparts. In this paper, we introduce an unsupervised local optical flow estimation method adapted to small moving objects in large-size images by involving no downsampling of the feature maps. We adopt a local correlation search and implement it in an original way with a per-shift computation, which minimizes memory consumption and speed up inference computation for large-scale images. We also design a loss function combining similarity, smoothness and sparsity constraints. We demonstrate the performance of our SMOFlow method on real stabilized aerial videos fully representative of future satellite conditions. SMOFlow favorably compares to other methods. Our SMOFlow method is able to accurately capture the motion of small objects in large images, while efficiently reducing memory consumption.

关键词： computer vision Solid modeling Correlation Satellites Memory management Estimation Satellite images

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DeSRF: Deformable Stylized Radiance Field

DeSRF: Deformable Stylized Radiance Field

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

作者： Shiyao Xu Lingzhi Li Li Shen Zhouhui Lian Wangxuan Institute of Computer Technology Peking University Alibaba Group Beijing China

When stylizing 3D scenes, current methods need to render the full-resolution images from different views and use the style loss, which is proposed for 2D style transfer and needs to be calculated on the whole image, to optimize the stylized radiance fields. It is quite inefficient when we need to stylize a large-scale scene. This paper proposes a more efficient method, DeSRF, to stylize the radiance fields, which also transfers style information to the geometry according to the input style. To achieve this goal, on the one hand, we first introduce a deformable module, which can learn the geometric style contained in the input style image and transfer it to radiance fields. On the other hand, although the style loss needs to be calculated for the entire image, actually we do not need to process all the rays when updating the stylized radiance fields. Motivated by this observation, we propose a new training strategy called Dilated Sampling (DS) for efficient stylization propagation. Experimental results show that our method works more efficiently and produces more visually-reasonable stylized 3D scenes with geometry style information compared to other existing approaches.

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Time Lens++: Event-based Frame Interpolation with Parametric Nonlinear Flow and Multi-scale Fusion

Time Lens++: Event-based Frame Interpolation with Parametric...

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Tulyakov, Stepan Bochicchio, Alfredo Gehrig, Daniel Georgoulis, Stamatios Li, Yuanyou Scaramuzza, Davide Huawei Technologies Zurich Research Center Switzerland Univ. of Zurich Eth Zurich Dept. of Informatics Dept. of Neuroinformatics Czech Republic

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory efficiency. However, current methods still suffer from (i) brittle image-level fusion of complementary interpolation results, that fails in the presence of artifacts in the fused image, (ii) potentially temporally inconsistent and inefficient motion estimation procedures, that run for every inserted frame and (iii) low contrast regions that do not trigger events, and thus cause events-only motion estimation to generate artifacts. Moreover, previous methods were only tested on datasets consisting of planar and far-away scenes, which do not capture the full complexity of the real world. In this work, we address the above problems by introducing multi-scale feature-level fusion and computing one-shot non-linear inter-frame motion-which can be efficiently sampled for image warping-from events and images. We also collect the first large-scale events and frames dataset consisting of more than 100 challenging scenes with depth variations, captured with a new experimental setup based on a beamsplitter. We show that our method improves the reconstruction quality by up to 0.2 dB in terms of PSNR and up to 15% in LPIPS score. © 2022 ieee.

关键词： Interpolation computer vision Motion estimation Memory management Dynamics Cameras pattern recognition

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Language-guided Multi-modal Emotional Mimicry Intensity Estimation

Language-guided Multi-modal Emotional Mimicry Intensity Esti...

引用

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

作者： Feng Qiu Wei Zhang Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Emotional Mimicry Intensity (EMI) estimation aims to identify the intensity of mimicry exhibited by individuals in response to observed emotions. The challenge in EMI estimation lies in discerning nuanced facial expression cues on mimicry behaviors based on the seed video and the text instructions. In this paper, we propose a multi-modal EMI estimation framework by leveraging visual, auditory, and textual modalities to capture a comprehensive emotional profile. We first extract representations for each modality separately and then fuse the modality-specific representations via a Temporal Segment Network, optimizing for temporal coherence and emotional context. Furthermore, we find that participants demonstrate notable proficiency in mimicking text instructions, yet exhibit less effectiveness in replicating facial expressions and vocal tones. In light of this, we design a contrastive learning mechanism to refine the extracted feature based on textual guidance. By doing so, features derived from similar text instructions are closely aligned, enhancing the estimation of emotional mimicry intensity by leveraging the dominant textual modality. Experiments conducted on the Hume-Vidmimic2 dataset illustrate the effectiveness of our framework in EMI estimation. Our framework is recognized as the leading solution in the Emotional Mimicry Intensity (EMI) Estimation Challenge at the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： Visualization Emotion recognition computer vision Fuses conferences Electromagnetic interference Estimation

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Latent Flow Diffusion for Deepfake Video Generation

Latent Flow Diffusion for Deepfake Video Generation

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

作者： Aashish Chandra K Aashutosh A V Srijan Das Abhijit Das Birla Institute of Technology & Science Pilani India University of North Carolina Charlotte USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image-to-video generation with conditional identity swap popularly known as deepfake, aims to synthesize a new video for the target identity guided by an image of the target and a video of the source identity. The biggest challenge of these tasks lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given target image and source video. In this paper, we propose a deepfake generation technique using novel latent flow diffusion (LFD) that includes an optical flow sequence in the latent space based on a given source video to warp the given target image. Compared to previous works on video diffusion, our proposed LFD can swap the spatial details maintaining temporal information by utilizing the spatial content of the given target image and employing the latent flow of the source video. Our model consists of three stages: a Flow predictor model captures the optical flow of the source video, two-fold Transformer encoding layers predict the driving frame and a conditioned image-to-video generator guided by the driving frame generates the final deep fake video. We conducted multiple experiments and our proposed model has consistently outperformed prior video diffusion models for deepfake generation.

关键词： Deepfakes computer vision Image motion analysis Image coding Predictive models Transformers Generators

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iEdit: Localised Text-guided Image Editing with Weak Supervision

iEdit: Localised Text-guided Image Editing with Weak Supervi...

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

作者： Rumeysa Bodur Erhan Gundogdu Binod Bhattarai Tae-Kyun Kim Michael Donoser Loris Bazzani Imperial College London UK Amazon University of Aberdeen UK KAIST South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models (DMs) can generate realistic images with text guidance using large-scale datasets. However, they demonstrate limited controllability on the generated images. We introduce iEdit, a novel method for text-guided image editing conditioned on a source image and textual prompt. As a fully-annotated dataset with target images does not exist, previous approaches perform subject-specific fine-tuning at test time or adopt contrastive learning without a target image, leading to issues on preserving source image fidelity. We propose to automatically construct a dataset derived from LAION-5B, containing pseudo-target images and descriptive edit prompts. The dataset allows us to incorporate a weakly-supervised loss function, generating the pseudo-target image from the source image’s latent noise conditioned on the edit prompt. To encourage localised editing we propose a loss function that uses segmentation masks to guide the editing during training and optionally at inference. Trained with limited GPU resources on the constructed dataset, our model outperforms counterparts in image fidelity, CLIP alignment score, and qualitatively for both generated and real images.

关键词： Training Image segmentation computer vision conferences Noise Graphics processing units Contrastive learning

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How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

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

作者： Tommie Kerssies Daan De Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are fine-tuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to fine-tune the ViT-B variants of VFMs with a 16 × 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page 1 .

关键词： Training computer vision Analytical models Codes Semantic segmentation conferences Benchmark testing

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