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Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning

Ultra-High-Definition Image Dehazing via Multi-Guided Bilate...

引用

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

作者： Zheng, Zhuoran Ren, Wenqi Cao, Xiaochun Hu, Xiaobin Wang, Tao Song, Fenglong Jia, Xiuyi Nanjing Univ Sci & Technol CSE Nanjing Peoples R China Chinese Acad Sci IIE SKLOIS Beijing Peoples R China Tech Univ Munich Informat Munich Germany Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9781665445092

Convolutional neural networks (CNNs) have achieved significant success in the single image dehazing task. Unfortunately, most existing deep dehazing models have high computational complexity, which hinders their application to high-resolution images, especially for UHD (ultra-high-definition) or 4K resolution images. To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs. The first CNN extracts haze-relevant features at a reduced resolution of the hazy input and then fits locally-affine models in the bilateral space. Another CNN is used to learn multiple full-resolution guidance maps corresponding to the learned bilateral model. As a result, the feature maps with high-frequency can be reconstructed by multi-guided bilateral upsampling. Finally, the third CNN fuses the high-quality feature maps into a dehazed image. In addition, we create a large-scale 4K image dehazing dataset to support the training and testing of compared models. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art dehazing approaches on various benchmarks.

关键词： Training Image resolution Image edge detection Feature extraction Real-time systems pattern recognition Image restoration

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ABMDRNet: Adaptive-weighted Bi-directional Modality Difference Reduction Network for RGB-T Semantic Segmentation

ABMDRNet: Adaptive-weighted Bi-directional Modality Differen...

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

作者： Zhang, Qiang Zhao, Shenlu Luo, Yongjiang Zhang, Dingwen Huang, Nianchang Han, Jungong Xidian Univ Sch Mechano Elect Engn Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth Dyfed Wales

ISBN: (纸本)9781665445092

Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and weighted summation, to fuse features from different modalities. These strategies, unfortunately, overlook the modality differences due to different imaging mechanisms, so that they suffer from the reduced discriminability of the fused features. To address such an issue, we propose, for the first time, the strategy of bridging-then-fusing, where the innovation lies in a novel Adaptive-weighted Bi-directional Modality Difference Reduction Network (ABMDRNet). Concretely, a Modality Difference Reduction and Fusion (MDRF) subnetwork is designed, which first employs a bi-directional image-to-image translation based method to reduce the modality differences between RGB features and thermal features, and then adaptively selects those discriminative multi-modality features for RGB-T semantic segmentation in a channel-wise weighted fusion way. Furthermore, considering the importance of contextual information in semantic segmentation, a Multi-Scale Spatial Context (MSC) module and a Multi-Scale Channel Context (MCC) module are proposed to exploit the interactions among multi-scale contextual information of cross-modality features together with their long-range dependencies along spatial and channel dimensions, respectively. Comprehensive experiments on MFNet dataset demonstrate that our method achieves new state-of-the-art results.

关键词： Image segmentation Technological innovation computer vision Fuses Semantics Lighting Imaging

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Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval

Multi-Modal Relational Graph for Cross-Modal Video Moment Re...

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

作者： Zeng, Yawen Cao, Da Wei, Xiaochi Liu, Meng Zhao, Zhou Qin, Zheng Hunan Univ Changsha Hunan Peoples R China Baidu Inc Beijing Peoples R China Shandong Jianzhu Univ Jinan Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the interactions or alignments between different modalities. However, the task of cross-modal video moment retrieval is not yet thoroughly addressed as it needs to further identify the fine-grained differences of video moment candidates with high repeatability and similarity. Moveover, the relation among objects in both video and sentence is intuitive and efficient for understanding semantics but is rarely considered. Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates. Specifically, we first introduce a visual relational graph and a textual relational graph to form relation-aware representations via message propagation. Thereafter, a multi-task pre-training is designed to capture domain-specific knowledge about objects and relations, enhancing the structured visual representation after explicitly defined relation. Finally, the graph matching and boundary regression are employed to perform the cross-modal retrieval. We conduct extensive experiments on two datasets about daily activities and cooking activities, demonstrating significant improvements over state-of-the-art solutions.

关键词： Visualization computer vision Semantics pattern recognition Object recognition Task analysis

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Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

Adapt Before Comparison: A New Perspective on Cross-Domain F...

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conference on computer vision and pattern recognition (cvpr)

作者： Jonas Herzog Zhejiang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot segmentation performance declines substantially when facing images from a domain different than the training domain, effectively limiting real-world use cases. To alleviate this, recently cross-domain few-shot segmentation (CD-FSS) has emerged. Works that address this task mainly attempted to learn segmentation on a source domain in a manner that generalizes across domains. Surprisingly, we can outperform these approaches while eliminating the training stage and removing their main segmentation network. We show test-time task-adaption is the key for successful CD-FSS instead. Task-adaption is achieved by appending small networks to the feature pyramid of a conventionally classification-pretrained backbone. To avoid overfitting to the few labeled samples in supervised fine-tuning, consistency across augmented views of input images serves as guidance while learning the parameters of the attached layers. Despite our self-restriction not to use any images other than the few labeled samples at test time, we achieve new state-of-the-art performance in CD-FSS, evidencing the need to rethink approaches for the task. Code is available at https://***/vision-Kek/ABCDFSS.

关键词： Training Image segmentation computer vision Adaptation models Limiting Codes pattern recognition

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Adversarial Robust Model Compression using In-Train Pruning

Adversarial Robust Model Compression using In-Train Pruning

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

作者： Vemparala, Manoj-Rohit Fasfous, Nael Frickenstein, Alexander Sarkar, Sreetama Zhao, Qi Kuhn, Sabine Frickenstein, Lukas Singh, Anmol Unger, Christian Nagaraja, Naveen-Shankar Wressnegger, Christian Stechele, Walter BMW Autonomous Driving Munich Germany Tech Univ Munich Munich Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665448994

Efficiently deploying learning-based systems on embedded hardware is challenging for various reasons, two of which are considered in this paper: The model's size and its robustness against attacks. Both need to be addressed even-handedly. We combine adversarial training and model pruning in a joint formulation of the fundamental learning objective during training. Unlike existing post-train pruning approaches, our method does not use heuristics and eliminates the need for a pre-trained model. This allows for a classifier which is robust against attacks and enables better compression of the model, reducing its computational effort. In comparison to prior work, our approach yields 6.21 pp higher accuracy for an 85 % reduction in parameters for ResNet20 on the CIFAR-10 dataset.

关键词： Training computer vision Computational modeling conferences Robustness Hardware pattern recognition

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Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation

Semi-supervised Domain Adaptation based on Dual-level Domain...

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

作者： Chen, Shuaijun Jia, Xu He, Jianzhong Shi, Yongjie Liu, Jianzhuang Huawei Technol Noahs Ark Lab Shenzhen Guangdong Peoples R China Dalian Univ Technol Dalian Liaoning Peoples R China Huawei Cloud Data Storage & Intelligent Vis Tech Res Dept Shenzhen Guangdong Peoples R China Peking Univ Key Lab Machine Percept Beijing Peoples R China Noahs Ark Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Data-driven based approaches, in spite of great success in many tasks, have poor generalization when applied to unseen image domains, and require expensive cost of annotation especially for dense pixel prediction tasks such as semantic segmentation. Recently, both unsupervised domain adaptation (UDA) from large amounts of synthetic data and semi-supervised learning (SSL) with small set of labeled data have been studied to alleviate this issue. However, there is still a large gap on performance compared to their supervised counterparts. We focus on a more practical setting of semi-supervised domain adaptation (SSDA) where both a small set of labeled target data and large amounts of labeled source data are available. To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed. The proposed framework consists of three stages. First, two kinds of data mixing methods are proposed to reduce domain gap in both region-level and sample-level respectively. We can obtain two complementary domain-mixed teachers based on dual-level mixed data from holistic and partial views respectively. Then, a student model is learned by distilling knowledge from these two teachers. Finally, pseudo labels of unlabeled data are generated in a self-training manner for another few rounds of teachers training. Extensive experimental results have demonstrated the effectiveness of our proposed framework on synthetic-to-real semantic segmentation benchmarks.

关键词： Training Adaptation models Image segmentation computer vision Costs Semantics Benchmark testing

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Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Obj...

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

作者： Li, Bohao Yang, Boyu Liu, Chang Liu, Feng Ji, Rongrong Ye, Qixiang Univ Chinese Acad Sci EECE PriSDL Beijing 100049 Peoples R China Xiamen Univ MAC Dept Artificial Intelligence Sch Informat Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Few-shot object detection has made substantial progress by representing novel class objects using the feature representation learned upon a set of base class objects. However, an implicit contradiction between novel class classification and representation is unfortunately ignored. On the one hand, to achieve accurate novel class classification, the distributions of either two base classes must be far away from each other (max-margin). On the other hand, to precisely represent novel classes, the distributions of base classes should be close to each other to reduce the intra-class distance of novel classes (min-margin). In this paper, we propose a class margin equilibrium (CME) approach, with the aim to optimize both feature space partition and novel class reconstruction in a systematic way. CME first converts the few-shot detection problem to the few-shot classification problem by using a fully connected layer to decouple localization features. CME then reserves adequate margin space for novel classes by introducing simple-yet-effective class margin loss during feature learning. Finally, CME pursues margin equilibrium by disturbing the features of novel class instances in an adversarial min-max fashion. Experiments on Pascal VOC and MS-COCO datasets show that CME significantly improves upon two baseline detectors (up to 3 similar to 5% in average), achieving state-of-the-art performance.

关键词： Training Location awareness computer vision Systematics Codes Object detection Detectors

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STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack: Template-free Visual Tracking with Space-time Memo...

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

作者： Fu, Zhihong Liu, Qingjie Fu, Zehua Wang, Yunhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781665445092

Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time tracking and practical applications. In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame. Furthermore, the pixel-level similarity computation of the memory network enables our tracker to generate much more accurate bounding boxes of the target. Extensive experiments and comparisons with many competitive trackers on challenging large-scale benchmarks, OTB-2015, TrackingNet, GOT-10k, LaSOT, UAV123, and VOT2018, show that, without bells and whistles, our tracker outperforms all previous state-of-the-art real-time methods while running at 37 FPS.

关键词： Visualization computer vision Target tracking Codes Adaptive systems Benchmark testing Boosting

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Generalizing Face Forgery Detection with High-frequency Features

Generalizing Face Forgery Detection with High-frequency Feat...

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

作者： Luo, Yuchen Zhang, Yong Yan, Junchi Liu, Wei Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence AI Inst Shanghai Peoples R China Tencent AI Lab Bellevue WA USA Tencent Data Platform Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms. In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection. We carefully devise three functional modules to take full advantage of the high-frequency features. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective. The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive evaluations on several benchmark databases corroborate the superior generalization performance of our proposed method.

关键词： Training Correlation Image color analysis Databases Face recognition Detectors Feature extraction

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SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field

SurroundSDF: Implicit 3D Scene Understanding Based on Signed...

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conference on computer vision and pattern recognition (cvpr)

作者： Lizhe Liu Bohua Wang Hongwei Xie Daqi Liu Li Liu Zhiqiang Tian Kuiyuan Yang Bing Wang Xiaomi EV School of Software Engineering Xi'an Jiaotong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

vision-centric 3D environment understanding is both vi-tal and challenging for autonomous driving systems. Re-cently, object-free methods have attracted considerable at-tention. Such methods perceive the world by predicting the semantics of discrete voxel grids but fail to construct continuous and accurate obstacle surfaces. To this end, in this paper, we propose SurroundSDF to implicitly predict the signed distance field (SDF) and semantic field for the continuous perception from surround images. Specifically, we introduce a query-based approach and utilize SDF con-strained by the Eikonal formulation to accurately describe the surfaces of obstacles. Furthermore, considering the absence of precise SDF ground truth, we propose a novel weakly supervised paradigm for SDF, referred to as the Sandwich Eikonal formulation, which emphasizes applying correct and dense constraints on both sides of the surface, thereby enhancing the perceptual accuracy of the surface. Experiments suggest that our method achieves SOTA for both occupancy prediction and 3D scene reconstruction tasks on the nuScenes dataset.

关键词： Surface reconstruction computer vision Three-dimensional displays Accuracy Semantics Cameras pattern recognition

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