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Uncertainty-aware vision-based Metric Cross-view Geolocalization

Uncertainty-aware Vision-based Metric Cross-view Geolocaliza...

引用

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

作者： Fervers, Florian Bullinger, Sebastian Bodensteiner, Christoph Arens, Michael Stiefelhagen, Rainer Fraunhofer IOSB Ettlingen Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9798350301298

This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose. Since aerial images are globally available at low cost, they represent a potential compromise between two established paradigms of autonomous driving, i.e. using expensive high-definition prior maps or relying entirely on the sensor data captured at runtime. We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We combine multiple vehicle datasets with aerial images from orthophoto providers on which we demonstrate the feasibility of our method. Since the ground truth poses are often inaccurate w.r.t. the aerial images, we implement a pseudo-label approach to produce more accurate ground truth poses and make them publicly available. While previous works require training data from the target region to achieve reasonable localization accuracy (i.e. same-area evaluation), our approach overcomes this limitation and outperforms previous results even in the strictly more challenging cross-area case. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region, which highlights the model's potential for global-scale application. We further integrate the uncertainty-aware predictions in a tracking framework to determine the vehicle's trajectory over time resulting in a mean position error on KITTI-360 of 0.78m.

关键词： Autonomous driving

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FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion vision-Language Pre-training

FashionSAP: Symbols and Attributes Prompt for Fine-grained F...

引用

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

作者： Han, Yunpeng Zhang, Lisai Chen, Qingcai Chen, Zhijian Li, Zhonghua Yang, Jianxin Cao, Zhao Harbin Inst Technol Shenzhen Harbin Peoples R China PengCheng Lab Shenzhen Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Fashion vision-language pre-training models have shown efficacy for a wide range of downstream tasks. However, general vision-language pre-training models pay less attention to fine-grained domain features, while these features are important in distinguishing the specific domain tasks from general tasks. We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics. Firstly, we propose the fashion symbols, a novel abstract fashion concept layer, to represent different fashion items and to generalize various kinds of fine-grained fashion features, making modelling fine-grained attributes more effective. Secondly, the attributes prompt method is proposed to make the model learn specific attributes of fashion items explicitly. We design proper prompt templates according to the format of fashion data. Comprehensive experiments are conducted on two public fashion benchmarks, i.e., FashionGen and FashionIQ, and FashionSAP gets SOTA performances for four popular fashion tasks. The ablation study also shows the proposed abstract fashion symbols, and the attribute prompt method enables the model to acquire fine-grained semantics in the fashion domain effectively. The obvious performance gains from FashionSAP provide a new baseline for future fashion task research.

关键词： Multi-modal learning

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Distilling Self-Supervised vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation

Distilling Self-Supervised Vision Transformers for Weakly-Su...

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

作者： Kang, Dahyun Koniusz, Piotr Cho, Minsu Murray, Naila Meta AI Menlo Pk CA 94025 USA POSTECH Pohang South Korea CSIRO Data61 Canberra Australia Australian Natl Univ Canberra Australia

ISBN: (纸本)9798350301298

We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions through separate task heads. Our model is able to effectively learn to perform classification and segmentation in the absence of pixel-level labels during training, using only image-level labels. To do this it uses attention maps, created from tokens generated by the self-supervised ViT backbone, as pixel-level pseudo-labels. We also explore a practical setup with "mixed" supervision, where a small number of training images contains ground-truth pixel-level labels and the remaining images have only image-level labels. For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels. Experiments on Pascal-5(i) and COCO-20(i) demonstrate significant performance gains in a variety of supervision settings, and in particular when little-to-no pixel-level labels are available.

关键词： continual low-shot meta or long-tail learning Transfer

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OpenGait: Revisiting Gait recognition Toward Better Practicality

OpenGait: Revisiting Gait Recognition Toward Better Practica...

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

作者： Fan, Chao Liang, Junhao Shen, Chuanfu Hou, Saihui Huang, Yongzhen Yu, Shiqi Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China WATRIX AI Beijing Peoples R China

ISBN: (纸本)9798350301298

Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly,we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, GaitBase. Experimentally, we comprehensively compare GaitBase with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://***/ShiqiYu/OpenGait.

关键词： Biometrics

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Collaborative Static and Dynamic vision-Language Streams for Spatio-Temporal Video Grounding

Collaborative Static and Dynamic Vision-Language Streams for...

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

作者： Lin, Zihang Tan, Chaolei Hu, Jian-Fang Jin, Zhi Ye, Tiancai Zheng, Wei-Shi Sun Yat Sen Univ Guangzhou Peoples R China Tencent Shenzhen Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language description, which requires effective joint modeling of spatio-temporal visual-linguistic dependencies. In this work, we propose a novel framework in which a static vision-language stream and a dynamic vision-language stream are developed to collaboratively reason the target tube. The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intra-frame visual cues like object appearances. The dynamic stream models visual-linguistic dependencies across multiple consecutive frames to capture dynamic cues like motions. We further design a novel cross-stream collaborative block between the two streams, which enables the static and dynamic streams to transfer useful and complementary information from each other to achieve collaborative reasoning. Experimental results show the effectiveness of the collaboration of the two streams and our overall framework achieves new state-of-the-art performance on both HCSTVG and VidSTG datasets.

关键词： language reasoning vision

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MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action recognition

MoLo: Motion-augmented Long-short Contrastive Learning for F...

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

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force long-range temporal perception;ii) explicit motion learning is usually ignored, leading to partial information loss. To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder. Specifically, the long-short contrastive objective is to endow local frame features with long-form temporal awareness by maximizing their agreement with the global token of videos belonging to the same class. The motion autodecoder is a lightweight architecture to reconstruct pixel motions from the differential features, which explicitly embeds the network with motion dynamics. By this means, MoLo can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching. To demonstrate the effectiveness, we evaluate MoLo on five standard benchmarks, and the results show that MoLo favorably outperforms recent advanced methods. The source code is available at https://***/alibaba-mmai-research/MoLo.

关键词： Video: Action and event understanding

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DSTCFuse: A Method based on Dual-cycled Cross-awareness of Structure Tensor for Semantic Segmentation via Infrared and Visible Image Fusion

DSTCFuse: A Method based on Dual-cycled Cross-awareness of S...

引用

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

作者： Li, Xuan Chen, Rongfu Wang, Jie Ma, Lei Cheng, Li Yuan, Haiwen Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Peoples R China Hubei Key Lab Opt Informat & Pattern Recognit Wuhan Peoples R China

ISBN: (纸本)9798350365474

Multi-modality information fusion can compensate deficiencies of single modality and provide rich scene information for 2D semantic segmentation. However, the inconsistency in the feature space between different modalities may lead to poor presentation of objects and that would affect subsequent segmented effectiveness. The idea of modal transition can reduce the modal differences and avoid biased processing during the fusion process, but it is hard to perfectly retain the contents of the source images. To address these challenges, a fusion method based on dual-cycled cross-awareness of structure tensor is proposed. Firstly, we propose a dual-cycle modality transition network based on cross-awareness consistency to learn the differences in feature space from different modalities. Secondly, a set of globally structure-tensor preserving modules are designed to enhance the capabilities of the network in capturing complementary features and perceiving global modal consistency. Under the joint constraint of globally structure-tensor awareness loss and cross-awareness loss, our network achieves a robust mapping of feature space from visible to pseudo-infrared images without relying on Ground-Truth. Finally, the pseudo-infrared images that inherit the superior qualities of two modalities are fused with the original infrared images directly, which effectively reduces the complexity of fusion. Extensive comparative experiments show that our method outperforms state-of-the-art methods in qualitative and quantitative evaluation.

关键词： Cross-modality transition Global structure-tensor awareness Image fusion

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Prototype-based Embedding Network for Scene Graph Generation

Prototype-based Embedding Network for Scene Graph Generation

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

作者： Zheng, Chaofan Lyu, Xinyu Gao, Lianli Dai, Bo Song, Jingkuan Univ Elect Sci & Technol China Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category, e.g., "man-eating-pizza, giraffe-eating-leaf", and the severe inter-class similarity between different classes, e.g., "man-holding-plate, man-eating-pizza", in model's latent space. The above challenges prevent current SGG methods from acquiring robust features for reliable relation prediction. In this paper, we claim that the predicate's category-inherent semantics can serve as class-wise prototypes in the semantic space for relieving the challenges. To the end, we propose the Prototype-based Embedding Network (PE-Net), which models entities/predicates with prototype-aligned compact and distinctive representations and thereby establishes matching between entity pairs and predicates in a common embedding space for relation recognition. Moreover, Prototype-guided Learning (PL) is introduced to help PE-Net efficiently learn such entity-predicate matching, and Prototype Regularization (PR) is devised to relieve the ambiguous entity-predicate matching caused by the predicate's semantic overlap. Extensive experiments demonstrate that our method gains superior relation recognition capability on SGG, achieving new state-of-the-art performances on both Visual Genome and Open Images datasets. The codes are available at https://***/VL- Group/PENET.

关键词： and reasoning language vision

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Accelerating vision-Language Pretraining with Free Language Modeling

Accelerating Vision-Language Pretraining with Free Language ...

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

作者： Wang, Teng Ge, Yixiao Zheng, Feng Cheng, Ran Shan, Ying Qie, Xiaohu Luo, Ping Southern Univ Sci & Technol Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China ARC Lab Shenzhen Peoples R China Tencent PCG Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of tokens for reconstruction) and corruption rate (percentage of corrupted tokens) in masked language modeling (MLM), that is, a proper corruption rate is achieved at the cost of a large portion of output tokens being excluded from prediction loss. To accelerate the convergence of VLP, we propose a new pretraining task, namely, free language modeling (FLM), that enables a 100% prediction rate with arbitrary corruption rates. FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted. FLM-trained models are encouraged to learn better and faster given the same GPU time by exploiting bidirectional contexts more flexibly. Extensive experiments show FLM could achieve an impressive 2.5x pretraining time reduction in comparison to the MLM-based methods, while keeping competitive performance on both vision-language understanding and generation tasks. Code will be public at https://***/TencentARC/FLM.

关键词： and reasoning language vision

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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...

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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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