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Learning Fine-Grained Segmentation of 3D Shapes without Part Labels

Learning Fine-Grained Segmentation of 3D Shapes without Part...

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

作者： Wang, Xiaogang Sun, Xun Cao, Xinyu Xu, Kai Zhou, Bin Southwest Univ Chongqing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781665445092

Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. This assumption, however, is impractical for learning fine-grained segmentation. Although most off-the-shelf CAD models are, by construction, composed of fine-grained parts, they usually miss semantic tags and labeling those fine-grained parts is extremely tedious. We approach the problem with deep clustering, where the key idea is to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part segmentation through minimizing a novel low rank loss. To handle highly densely sampled point sets, we adopt a divide-and-conquer strategy. We partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network trained in a category-agnostic manner. We then train a graph convolution network to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.

关键词： Training Solid modeling Three-dimensional displays Shape Semantics Graph neural networks pattern recognition

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LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents

LaPred: Lane-Aware Prediction of Multi-Modal Future Trajecto...

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

作者： Kim, Byeoung Do Park, Seong Hyeon Lee, Seokhwan Khoshimjonov, Elbek Kum, Dongsuk Kim, Junsoo Kim, Jeong Soo Choi, Jun Won Hanyang Univ Seoul South Korea Korea Adv Inst Sci & Technol Seoul South Korea Hyundai Motor Co Seoul South Korea

ISBN: (纸本)9781665445092

In this paper, we address the problem of predicting the future motion of a dynamic agent (called a target agent) given its current and past states as well as the information on its environment. It is paramount to develop a prediction model that can exploit the contextual information in both static and dynamic environments surrounding the target agent and generate diverse trajectory samples that are meaningful in a traffic context. We propose a novel prediction model, referred to as the lane-aware prediction (LaPred) network, which uses the instance-level lane entities extracted from a semantic map to predict the multi-modal future trajectories. For each lane candidate found in the neighborhood of the target agent, LaPred extracts the joint features relating the lane and the trajectories of the neighboring agents. Then, the features for all lane candidates are fused with the attention weights learned through a self-supervised learning task that identifies the lane candidate likely to be followed by the target agent. Using the instance-level lane information, LaPred can produce the trajectories compliant with the surroundings better than 2D raster image-based methods and generate the diverse future trajectories given multiple lane candidates. The experiments conducted on the public nuScenes dataset and Argoverse dataset demonstrate that the proposed LaPred method significantly outperforms the existing prediction models, achieving state-of-the-art performance in the benchmarks.

关键词： Measurement Dynamics Semantics Predictive models Feature extraction Trajectory pattern recognition

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Semi-Supervised Video Deraining with Dynamical Rain Generator

Semi-Supervised Video Deraining with Dynamical Rain Generato...

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

作者： Yue, Zongsheng Xie, Jianwen Zhao, Qian Meng, Deyu Xi An Jiao Tong Univ Xian Peoples R China Baidu Res Cognit Comp Lab Bellevue WA USA Macau Univ Sci & Technol Macau Peoples R China

ISBN: (纸本)9781665445092

While deep learning (DL)-based video deraining methods have achieved significant successes in recent years, they still have two major drawbacks. Firstly, most of them are insufficient to model the characteristics of rain layers contained in rainy videos. In fact, the rain layers exhibit strong visual properties (e.g., direction, scale, and thickness) in spatial dimension and causal properties (e.g., velocity and acceleration) in temporal dimension, and thus can be modeled by the spatial-temporal process in statistics. Secondly, current DL-based methods rely heavily on the labeled training data, whose rain layers are synthetic, thus leading to a deviation from real data. Such a gap between synthetic and real data sets results in poor performance when applying them to real scenarios. To address these issues, this paper proposes a new semi-supervised video deraining method, in which a dynamical rain generator is employed to fit the rain layer for the sake of better depicting its intrinsic characteristics. Specifically, the dynamical generator consists of one emission model and one transition model to simultaneously encode the spatial appearance and temporal dynamics of rain streaks, respectively, both of which are parameterized by deep neural networks (DNNs). Furthermore, different prior formats are designed for the labeled synthetic and unlabeled real data so as to fully exploit their underlying common knowledge. Last but not least, we design a Monte Carlo-based EM algorithm to learn the model. Extensive experiments are conducted to verify the superiority of the proposed semi-supervised deraining model.

关键词： Deep learning Visualization computer vision Rain Monte Carlo methods Heuristic algorithms Training data

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

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

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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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