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On the Difficulty of Unpaired Infrared-to-Visible Video Translation: Fine-Grained Content-Rich Patches Transfer

On the Difficulty of Unpaired Infrared-to-Visible Video Tran...

引用

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

作者： Yu, Zhenjie Li, Shuang Shen, Yirui Liu, Chi Harold Wang, Shuigen Beijing Inst Technol Beijing Peoples R China Yantai IRay Technol Ltd Co Yantai Peoples R China

ISBN: (纸本)9798350301298

Explicit visible videos can provide sufficient visual information and facilitate vision applications. Unfortunately, the image sensors of visible cameras are sensitive to light conditions like darkness or overexposure. To make up for this, recently, infrared sensors capable of stable imaging have received increasing attention in autonomous driving and monitoring. However, most prosperous vision models are still trained on massive clear visible data, facing huge visual gaps when deploying to infrared imaging scenarios. In such cases, transferring the infrared video to a distinct visible one with fine-grained semantic patterns is a worthwhile endeavor. Previous works improve the outputs by equally optimizing each patch on the translated visible results, which is unfair for enhancing the details on content-rich patches due to the long-tail effect of pixel distribution. Here we propose a novel CPTrans framework to tackle the challenge via balancing gradients of different patches, achieving the fine-grained Content-rich Patches Transferring. Specifically, the content-aware optimization module encourages model optimization along gradients of target patches, ensuring the improvement of visual details. Additionally, the content-aware temporal normalization module enforces the generator to be robust to the motions of target patches. Moreover, we extend the existing dataset InfraredCity to more challenging adverse weather conditions (rain and snow), dubbed as InfraredCity-Adverse1. Extensive experiments show that the proposed CPTrans achieves state-of-the-art performance under diverse scenes while requiring less training time than competitive methods.

关键词： computer vision for social good

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CoVIO: Online Continual Learning for Visual-Inertial Odometry

CoVIO: Online Continual Learning for Visual-Inertial Odometr...

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

作者： Vödisch, Niclas Cattaneo, Daniele Burgard, Wolfram Valada, Abhinav University of Freiburg Germany University of Technology Nuremberg Germany

ISBN: (纸本)9798350302493

Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems are known to generalize poorly to unseen environments, methods for continual adaptation during inference time are of significant interest. In this work, we introduce CoVIO for online continual learning of visual-inertial odometry. CoVIO effectively adapts to new domains while mitigating catastrophic forgetting by exploiting experience replay. In particular, we propose a novel sampling strategy to maximize image diversity in a fixed-size replay buffer that targets the limited storage capacity of embedded devices. We further provide an asynchronous version that decouples the odometry estimation from the network weight update step enabling continuous inference in real time. We extensively evaluate CoVIO on various real-world datasets demonstrating that it successfully adapts to new domains while outperforming previous methods. The code of our work is publicly available at http://***. © 2023 ieee.

关键词： computer vision

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Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior

Video Dehazing via a Multi-Range Temporal Alignment Network ...

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

作者： Xu, Jiaqi Hu, Xiaowei Zhu, Lei Dou, Qi Dai, Jifeng Qiao, Yu Heng, Pheng-Ann Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggregate temporal information. Specifically, we design a memory-based physical prior guidance module to encode the prior-related features into long-range memory. Besides, we formulate a multi-range scene radiance recovery module to capture space-time dependencies in multiple space-time ranges, which helps to effectively aggregate temporal information from adjacent frames. Moreover, we construct the first large-scale outdoor video dehazing benchmark dataset, which contains videos in various real-world scenarios. Experimental results on both synthetic and real conditions show the superiority of our proposed method.

关键词： Low-level vision

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EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Vis...

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

作者： Wu, Yanmin Cheng, Xinhua Zhang, Renrui Cheng, Zesen Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues. However, existing methods either extract the sentence-level features coupling all words or focus more on object names, which would lose the word-level information or neglect other attributes. To alleviate these issues, we present EDA that Explicitly Decouples the textual attributes in a sentence and conducts Dense Alignment between such fine-grained language and point cloud objects. Specifically, we first propose a text decoupling module to produce textual features for every semantic component. Then, we design two losses to supervise the dense matching between two modalities: position alignment loss and semantic alignment loss. On top of that, we further introduce a new visual grounding task, locating objects without object names, which can thoroughly evaluate the model's dense alignment capacity. Through experiments, we achieve state-of-the-art performance on two widely-adopted 3D visual grounding datasets, Scan-Refer and SR3D/NR3D, and obtain absolute leadership on our newly-proposed task. The source code is available at https://***/yanmin-wu/EDA.

关键词： language reasoning vision

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Optimization-Inspired Cross-Attention Transformer for Compressive Sensing

Optimization-Inspired Cross-Attention Transformer for Compre...

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

作者： Song, Jiechong Mou, Chong Wang, Shiqi Ma, Siwei Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS). However, existing DUNs often improve the visual quality at the price of a large number of parameters and have the problem of feature information loss during iteration. In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, leading to a lightweight OCT-based Unfolding Framework (OCTUF) for image CS. Specifically, we design a novel Dual Cross Attention (Dual-CA) sub-module, which consists of an Inertia-Supplied Cross Attention (ISCA) block and a Projection-Guided Cross Attention (PGCA) block. ISCA block introduces multi-channel inertia forces and increases the memory effect by a cross attention mechanism between adjacent iterations. And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block. Extensive CS experiments manifest that our OCTUF achieves superior performance compared to state-of-the-art methods while training lower complexity. Codes are available at https://***/songjiechong/OCTUF.

关键词： Low-level vision

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Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics i...

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23rd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Melzi, Pietro Shahreza, Hatef Otroshi Rathgeb, Christian Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Marcel, Sebastien Busch, Christoph Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab Madrid Spain Idiap Res Inst Biometr Secur & Privacy Grp Martigny Switzerland Ecole Polytech Fed Lausanne Sch Engn Lausanne Switzerland Hsch Darmstadt HDA Biometr & Secur Res Grp Da Sec Darmstadt Germany Univ Lausanne UNIL Sch Criminal Justice Lausanne Switzerland Norwegian Univ Sci & Technol NTNU Norwegian Biometr Lab Trondheim Norway

ISBN: (纸本)9798350320565

This study focuses on the protection of soft-biometric attributes related to the demographic information of individuals that can be extracted from compact representations of face images, called embeddings. We consider a state-ofthe-art technology for soft-biometric privacy enhancement, Incremental Variable Elimination (IVE), and propose MultiIVE, a new method based on IVE to secure multiple softbiometric attributes simultaneously. Several aspects of this technology are investigated, proposing different approaches to effectively identify and discard multiple soft-biometric attributes contained in face embeddings. In particular, we consider a domain transformation using Principle Component Analysis (PCA), and apply IVE in the PCA domain. A complete analysis of the proposed Multi-IVE algorithm is carried out studying the embeddings generated by stateof-the-art face feature extractors, predicting soft-biometric attributes contained within them with multiple machine learning classifiers, and providing a cross-database evaluation. The results obtained show the possibility to simultaneously secure multiple soft-biometric attributes and support the application of embedding domain transformations before addressing the enhancement of soft-biometric privacy.

关键词： Privacy Machine learning algorithms Face recognition Machine learning Feature extraction Prediction algorithms Classification algorithms

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Uncovering the Missing pattern: Unified Framework Towards Trajectory Imputation and Prediction

Uncovering the Missing Pattern: Unified Framework Towards Tr...

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

作者： Xu, Yi Bazarjani, Armin Chi, Hyung-gun Choi, Chiho Fu, Yun Northeastern Univ Boston MA 02115 USA Honda Res Inst Mountain View CA 94043 USA Univ Southern Calif Los Angeles CA USA Purdue Univ W Lafayette IN USA Samsung Semicond US San Jose CA USA

ISBN: (纸本)9798350301298

Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed sequences are complete while ignoring the potential for missing values caused by object occlusion, scope limitation, sensor failure, etc. This limitation inevitably hinders the accuracy of trajectory prediction. To address this issue, our paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously. Specifically, we introduce a novel Multi-Space Graph Neural Network (MS-GNN) that can extract spatial features from incomplete observations and leverage missing patterns. Additionally, we employ a Conditional VRNN with a specifically designed Temporal Decay (TD) module to capture temporal dependencies and temporal missing patterns in incomplete trajectories. The inclusion of the TD module allows for valuable information to be conveyed through the temporal flow. We also curate and benchmark three practical datasets for the joint problem of trajectory imputation and prediction. Extensive experiments verify the exceptional performance of our proposed method. As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner.

关键词： and tracking motion Video: Low-level analysis

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

引用

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

作者： Tang, Chuan Yang, Xi Wu, Bojian Han, Zhizhong Chang, Yi Jilin Univ Sch Artificial Intelligence Jilin Peoples R China Zhejiang Univ Hangzhou Peoples R China Wayne State Univ Dept Comp Sci Wayne NJ USA MoE Engn Res Ctr Knowledge Driven Human Machine Intel Beijing Peoples R China

ISBN: (纸本)9798350301298

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

关键词： vision + graphics

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HOICLIP: Efficient Knowledge Transfer for HOI Detection with vision-Language Models

HOICLIP: Efficient Knowledge Transfer for HOI Detection with...

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

作者： Ning, Shan Qiu, Longtian Liu, Yongfei He, Xuming ShanghaiTech Univ Shanghai Peoples R China ByteDance Inc Beijing Peoples R China Shanghai Engn Res Ctr Intelligent Vis & 1maging Shanghai Peoples R China

ISBN: (纸本)9798350301298

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scale training data and suffer from inferior performance under few/zero-shot scenarios. In this paper, we propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization. In detail, we first introduce a novel interaction decoder to extract informative regions in the visual feature map of CLIP via a cross-attention mechanism, which is then fused with the detection backbone by a knowledge integration block for more accurate human-object pair detection. In addition, prior knowledge in CLIP text encoder is leveraged to generate a classifier by embedding HOI descriptions. To distinguish fine-grained interactions, we build a verb classifier from training data via visual semantic arithmetic and a lightweight verb representation adapter. Furthermore, we propose a training-free enhancement to exploit global HOI predictions from CLIP. Extensive experiments demonstrate that our method outperforms the state of the art by a large margin on various settings, e.g. +4.04 mAP on HICO-Det. The source code is available in https://***/Artanic30/HOICLIP.

关键词： detection recognition: Categorization retrieval

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OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer

OSRT: Omnidirectional Image Super-Resolution with Distortion...

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

作者： Yu, Fanghua Wang, Xintao Cao, Mingdeng Li, Gen Shan, Ying Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Tencent PCG ARC Shenzhen Peoples R China Univ Tokyo Tokyo Japan Tencent Online Video Platform Technol Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODISR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT.

关键词： Low-level vision

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