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Deep-FlexISP: A Three-Stage Framework for Night Photography Rendering

Deep-FlexISP: A Three-Stage Framework for Night Photography ...

引用

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

作者： Liu, Shuai Feng, Chaoyu Wang, Xiaotao Wang, Hao Zhu, Ran Li, Yongqiang Lei, Lei Xiaomi Inc Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Night photography rendering is challenging due to images' high noise level, less vivid color, and low dynamic range. In this work, we propose a three-stage cascade framework named Deep-FlexISP, which decomposes the ISP into three weakly correlated sub-tasks: raw image denoising, white balance, and Bayer to sRGB mapping, for the following considerations. First, task decomposition can enhance the learning ability of the framework and make it easier to converge. Second, weak correlation sub-tasks do not influence each other too much, so the framework has a high degree of freedom. Finally, noise, color, and brightness are essential for night photographs. Our framework can flexibly adjust different styles according to personal preferences with the vital learning ability and the degree of freedom. Compared with the other Deep-ISP methods, our proposed Deep-FlexISP shows state-of-the-art performance and achieves first place in people's choice and photographer's choice in NTIRE 2022 Night Photography Render Challenge.

关键词： Photography Image color analysis Brightness Noise reduction Rendering (computer graphics) pattern recognition Task analysis

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MMVP: A Multimodal MoCap Dataset with vision and Pressure Sensors

MMVP: A Multimodal MoCap Dataset with Vision and Pressure Se...

引用

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

作者： Zhang, He Ren, Shenghao Yuan, Haolei Zhao, Jianhui Li, Fan Sun, Shuangpeng Liang, Zhenghao Yu, Tao Shen, Qiu Cao, Xun Beihang Univ Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Beijing Weilan Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350353006

Foot contact is an important cue for human motion capture, understanding, and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However, these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion, as well as accurate and dense foot-contact annotation. To fill this gap, we propose a Multimodal MoCap Dataset with vision and Pressure sensors, named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations, which is especially useful for both plausible shape estimation, robust pose fitting without foot drifting, and accurate global translation tracking. To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover, VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://***/MMVP-Dataset/

关键词： foot contact Motion capture multimodal pressure

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An Attention-based Method for Multi-label Facial Action Unit Detection

An Attention-based Method for Multi-label Facial Action Unit...

引用

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

作者： Le Hoai, Duy Lim, Eunchae Choi, Eunbin Kim, Sieun Pant, Sudarshan Lee, Guee-Sang Kim, Soo-Huyng Yang, Hyung-Jeong Chonnam Natl Univ Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial Action Coding System is an approach for modeling the complexity of human emotional expression. Automatic action unit (AU) detection is a crucial research area in human-computer interaction. This paper describes our submission to the third Affective Behavior Analysis in-the-wild (ABAW) competition 2022. We proposed a method for detecting facial action units in the video. In the first stage, a lightweight CNN-based feature extractor is employed to extract the feature map from each video frame. Then, an attention module is applied to refine the attention map. The attention encoded vector is derived using a weighted sum of the feature map and the attention scores later. Finally, the sigmoid function is used at the output layer to make the prediction suitable for multi-label AUs detection. We achieved a macro F1 score of 0.48 on the validation set and 0.4206 on the test set compared to 0.39 and 0.3650 from the ABAW challenge baseline model.

关键词： Human computer interaction Gold computer vision conferences Computational modeling Feature extraction Encoding

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MoPE-CLIP: Structured Pruning for Efficient vision-Language Models with Module-wise Pruning Error Metric

MoPE-CLIP: Structured Pruning for Efficient Vision-Language ...

引用

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

作者： Lin, Haokun Bai, Haoli Liu, Zhili Hou, Lu Sung, Muyi Song, Linqi Wei, Ying Sun, Zhenan Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci CRIPAC Beijing Peoples R China Chinese Acad Sci MAIS Inst Automat Beijing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China City Univ Hong Kong Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350353006

vision-language pre-trained models have achieved impressive performance on various downstream tasks. However, their large model sizes hinder their utilization on platforms with limited computational resources. We find that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior performance. Recent efforts for VLP compression either adopt uni-modal compression metrics resulting in limited performance or involve costly mask-search processes with learnable masks. In this paper, we first propose the Module-wise Pruning Error (MoPE) metric, accurately assessing CLIP module importance by performance decline on cross-modal tasks. Using the MoPE metric, we introduce a unified pruning framework applicable to both pre-training and task-specific fine-tuning compression stages. For pre-training, MoPE-CLIP effectively leverages knowledge from the teacher model, significantly reducing pre-training costs while maintaining strong zero-shot capabilities. For fine-tuning, consecutive pruning from width to depth yields highly competitive task-specific models. Extensive experiments in two stages demonstrate the effectiveness of the MoPE metric, and MoPE-CLIP outperforms previous state-of-the-art VLP compression methods.

关键词： CLIP Model Compression Structured Pruning vision-Language Models

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Weakly-Supervised Action Detection Guided by Audio Narration

Weakly-Supervised Action Detection Guided by Audio Narration

引用

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

作者： Ye, Keren Kovashka, Adriana Cruise San Francisco CA 94107 USA Univ Pittsburgh Pittsburgh PA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Videos are more well-organized curated data sources for visual concept learning than images. Unlike the 2-dimensional images which only involve the spatial information, the additional temporal dimension bridges and synchronizes multiple modalities. However, in most video detection benchmarks, these additional modalities are not fully utilized. For example, EPIC Kitchens is the largest dataset in first-person (egocentric) vision, yet it still relies on crowdsourced information to refine the action boundaries to provide instance-level action annotations. We explored how to eliminate the expensive annotations in video detection data which provide refined boundaries. We propose a model to learn from the narration supervision and utilize multimodal features, including RGB, motion flow, and ambient sound. Our model learns to attend to the frames related to the narration label while suppressing the irrelevant frames from being used. Our experiments show that noisy audio narration suffices to learn a good action detection model, thus reducing annotation expenses.

关键词： Visualization Annotations Soft sensors Refining Detectors pattern recognition Synchronization

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RGB no more: Minimally-decoded JPEG vision Transformers

RGB no more: Minimally-decoded JPEG Vision Transformers

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

作者： Park, Jeongsoo Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9798350301298

Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk;decoding them imposes an unavoidable overhead for RGB networks. Instead, our work focuses on training vision Transformers (ViT) directly from the encoded features of JPEG. This way, we can avoid most of the decoding overhead, accelerating data load. Existing works have studied this aspect but they focus on CNNs. Due to how these encoded features are structured, CNNs require heavy modification to their architecture to accept such data. Here, we show that this is not the case for ViTs. In addition, we tackle data augmentation directly on these encoded features, which to our knowledge, has not been explored in-depth for training in this setting. With these two improvements - ViT and data augmentation - we show that our ViT-Ti model achieves up to 39.2% faster training and 17.9% faster inference with no accuracy loss compared to the RGB counterpart. (1)

关键词： and reasoning language vision

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ViTOL: vision Transformer for Weakly Supervised Object Localization

ViTOL: Vision Transformer for Weakly Supervised Object Local...

引用

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

作者： Gupta, Saurav Lakhotia, Sourav Rawat, Abhay Tallamraju, Rahul Mercedes Benz Res & Dev India Bengaluru India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Weakly supervised object localization (WSOL) aims at predicting object locations in an image using only image-level category labels. Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localization map to a very small region, (b) the localization maps are class agnostic, and the models highlight objects of multiple classes in the same image and, (c) the localization performance is affected by background noise. To alleviate the above challenges we introduce the following simple changes through our proposed method ViTOL. We leverage the vision-based transformer for self-attention and introduce a patch-based attention dropout layer (p-ADL) to increase the coverage of the localization map and a gradient attention rollout mechanism to generate class-dependent attention maps. We conduct extensive quantitative, qualitative and ablation experiments on the ImageNet-1K and CUB datasets. We achieve state-of-the-art MaxBoxAcc-V2 localization scores of 70.47% and 73.17% on the two datasets respectively.

关键词： Location awareness Measurement Visualization Shape Semantics computer architecture Transformers

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A Neural-network Enhanced Video Coding Framework beyond VVC

A Neural-network Enhanced Video Coding Framework beyond VVC

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

作者： Li, Junru Li, Yue Lin, Chaoyi Zhang, Kai Zhang, Li Bytedance Inc Multimedia Lab San Diego CA 92122 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a hybrid video compression framework, aiming at providing a demonstration of applying deep learning-based approaches beyond conventional coding framework. The proposed hybrid framework is established over the Enhanced Compression Model (ECM) of which the core is the Versatile Video Coding (VVC) standard. We propose to integrate a series of enhanced coding tools, such as block partitioning, intra prediction, and inter prediction to further remove the spatial and temporal redundancy. Moreover, deep learning-based technologies including loop filter and super resolution are involved to restore the compression distortion. Compared with the VVC software VTM-11.0, experimental results demonstrate the effectiveness of the proposed learning-based framework, leading to 25.81%, 35.08%, and 37.54% bit-rate savings for Y, Cb and Cr components, respectively under random access configuration. In addition, the proposed framework achieves 39.313 and 32.050 PSNRs in the test set under 1 Mbps and 0.1 Mbps video compression tracks of CLIC-2022. 33.522, 30.758, and 28.300 in terms of PSNR are obtained in 0.3 bpp, 0.15 bpp, and 0.075 bpp image compression tracks.

关键词： Video coding Image coding Filtering Redundancy Video compression Software pattern recognition

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ResNeSt: Split-Attention Networks

ResNeSt: Split-Attention Networks

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

作者： Zhang, Hang Wu, Chongruo Zhang, Zhongyue Zhu, Yi Lin, Haibin Zhang, Zhi Sun, Yue He, Tong Mueller, Jonas Manmatha, R. Li, Mu Smola, Alexander Meta Menlo Pk CA 94025 USA Univ Calif Davis Davis CA USA Snap Santa Monica CA USA Amazon Seattle WA USA ByteDance Beijing Peoples R China SenseTime Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The ability to learn richer network representations generally boosts the performance of deep learning models. To improve representation-learning in convolutional neural networks, we present a multi-branch architecture, which applies channel-wise attention across different network branches to leverage the complementary strengths of both feature-map attention and multi-path representation. Our proposed Split-Attention module provides a simple and modular computation block that can serve as a drop-in replacement for the popular residual block, while producing more diverse representations via cross-feature interactions. Adding a Split-Attention module into the architecture design space of RegNet-Y and FBNetV2 directly improves the performance of the resulting network. Replacing residual blocks with our Split-Attention module, we further design a new variant of the ResNet model, named ResNeSt, which outperforms EfficientNet in terms of the accuracy/latency trade-off.

关键词： Deep learning computer vision Computational modeling conferences computer architecture pattern recognition Convolutional neural networks

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Symmetric Network with Spatial Relationship Modeling for Natural Language-based Vehicle Retrieval

Symmetric Network with Spatial Relationship Modeling for Nat...

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

作者： Zhao, Chuyang Chen, Haobo Zhang, Wenyuan Chen, Junru Zhang, Sipeng Li, Yadong Li, Boxun MEGVII Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Natural language (NL) based vehicle retrieval aims to search specific vehicle given text description. Different from the image-based vehicle retrieval, NL-based vehicle retrieval requires considering not only vehicle appearance, but also surrounding environment and temporal relations. In this paper, we propose a Symmetric Network with Spatial Relationship Modeling (SSM) method for NL-based vehicle retrieval. Specifically, we design a symmetric network to learn the unified cross-modal representations between text descriptions and vehicle images, where vehicle appearance details and vehicle trajectory global information are preserved. Besides, to make better use of location information, we propose a spatial relationship modeling methods to take surrounding environment and mutual relationship between vehicles into consideration. The qualitative and quantitative experiments verify the effectiveness of the proposed method. We achieve 43.92% MRR accuracy on the test set of the 6th AI City Challenge on natural language-based vehicle retrieval track, yielding the 4th place on the public leaderboard.

关键词： Data privacy Visualization conferences Urban areas Linguistics Trajectory pattern recognition

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