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Uncertainty-aware vision-based Metric Cross-view Geolocalization

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

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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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Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations

Mask-free OVIS: Open-Vocabulary Instance Segmentation withou...

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

作者： Vibashan, V. S. Yu, Ning Xing, Chen Qin, Can Gao, Mingfei Nieblest, Juan Carlos Patel, Vishal M. Xu, Ran Johns Hopkins Univ Baltimore MD 21218 USA Northeastern Univ Boston MA 02115 USA Salesforce Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human effort, limiting the scalability to annotate novel (new) categories. To alleviate this problem, Open-Vocabulary (OV) methods leverage large-scale image-caption pairs and vision-language models to learn novel categories. In summary, an OV method learns task-specific information using strong supervision from base annotations and novel category information using weak supervision from image-captions pairs. This difference between strong and weak supervision leads to overfitting on base categories, resulting in poor generalization towards novel categories. In this work, we overcome this issue by learning both base and novel categories from pseudomask annotations generated by the vision-language model in a weakly supervised manner using our proposed Mask-free OVIS pipeline. Our method automatically generates pseudo-mask annotations by leveraging the localization ability of a pre-trained vision-language model for objects present in image-caption pairs. The generated pseudomask annotations are then used to supervise an instance segmentation model, freeing the entire pipeline from any labour-expensive instance-level annotations and overfitting. Our extensive experiments show that our method trained with just pseudo-masks significantly improves the mAP scores on the MS-COCO dataset and OpenImages dataset compared to the recent state-of-the-art methods trained with manual masks. Codes and models are provided in https://***/ovis-web/.

关键词： detection recognition: Categorization retrieval

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You Can Run but not Hide: Improving Gait recognition with Intrinsic Occlusion Type Awareness

You Can Run but not Hide: Improving Gait Recognition with In...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Gupta, Ayush Chellappa, Rama Johns Hopkins Univ 3400 North Charles St Baltimore MD 21218 USA

ISBN: (纸本)9798350318920;9798350318937

While gait recognition has seen many advances in recent years, the occlusion problem has largely been ignored. This problem is especially important for gait recognition from uncontrolled outdoor sequences at range - since any small obstruction can affect the recognition system. Most current methods assume the availability of complete body information while extracting the gait features. When parts of the body are occluded, these methods may hallucinate and output a corrupted gait signature as they try to look for body parts which are not present in the input at all. To address this, we exploit the learned occlusion type while extracting identity features from videos. Thus, in this work, we propose an occlusion aware gait recognition method which can be used to model intrinsic occlusion awareness into potentially any state-of-the-art gait recognition method. Our experiments on the challenging GREW and BRIAR datasets show that networks enhanced with this occlusion awareness perform better at recognition tasks than their counterparts trained on similar occlusions.

关键词： Algorithms Algorithms and algorithms Biometrics body pose face formulations gesture Machine learning architectures

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Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection

Instance Relation Graph Guided Source-Free Domain Adaptive O...

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

作者： Vibashan, V. S. Oza, Poojan Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21205 USA

ISBN: (纸本)9798350301298

Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. Specifically, UDA methods try to align the source and target representations to improve generalization on the target domain. Further, UDA methods work under the assumption that the source data is accessible during the adaptation process. However, in real-world scenarios, the labelled source data is often restricted due to privacy regulations, data transmission constraints, or proprietary data concerns. The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data. In this paper, we explore the SFDA setting for the task of adaptive object detection. To this end, we propose a novel training strategy for adapting a source-trained object detector to the target domain without source data. More precisely, we design a novel contrastive loss to enhance the target representations by exploiting the objects relations for a given target domain input. These object instance relations are modelled using an Instance Relation Graph (IRG) network, which are then used to guide the contrastive representation learning. In addition, we utilize a student-teacher to effectively distill knowledge from source-trained model to target domain. Extensive experiments on multiple object detection benchmark datasets show that the proposed approach is able to efficiently adapt source-trained object detectors to the target domain, outperforming state-of-the-art domain adaptive detection methods. Code and models are provided in https://***/irg-sfda-web/.

关键词： detection recognition: Categorization retrieval

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DualRel: Semi-Supervised Mitochondria Segmentation from A Prototype Perspective

DualRel: Semi-Supervised Mitochondria Segmentation from A Pr...

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

作者： Mai, Huayu Sun, Rui Zhang, Tianzhu Xiong, Zhiwei Wu, Feng Univ Sci & Technol China Langfang Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Deep Space Explorat Lab Hefei Peoples R China

ISBN: (纸本)9798350301298

Automatic mitochondria segmentation enjoys great popularity with the development of deep learning. However, existing methods rely heavily on the labor-intensive manual gathering by experienced domain experts. And naively applying semi-supervised segmentation methods in the natural image field to mitigate the labeling cost is undesirable. In this work, we analyze the gap between mitochondrial images and natural images and rethink how to achieve effective semi-supervised mitochondria segmentation, from the perspective of reliable prototype-level supervision. We propose a novel end-to-end dual-reliable (DualRel) network, including a reliable pixel aggregation module and a reliable prototype selection module. The proposed DualRel enjoys several merits. First, to learn the prototypes well without any explicit supervision, we carefully design the referential correlation to rectify the direct pairwise correlation. Second, the reliable prototype selection module is responsible for further evaluating the reliability of prototypes in constructing prototype-level consistency regularization. Extensive experimental results on three challenging benchmarks demonstrate that our method performs favorably against state-of-the-art semi-supervised segmentation methods. Importantly, with extremely few samples used for training, DualRel is also on par with current state-of-the-art fully supervised methods.

关键词： cell microscopy Medical and biological vision

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LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of vision & Language Models

LASP: Text-to-Text Optimization for Language-Aware Soft Prom...

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

作者： Bulat, Adrian Tzimiropoulos, Georgios Samsung AI Cambridge Toronto ON Canada Tech Univ Iasi Iasi Romania Qucen Maty Univ London London England

ISBN: (纸本)9798350301298

Soft prompt learning has recently emerged as one of the methods of choice for adapting V&L models to a downstream task using a few training examples. However, current methods significantly overfit the training data, suffering from large accuracy degradation when tested on unseen classes from the same domain. To this end, in this paper, we make the following 4 contributions: (1) To alleviate base class overfitting, we propose a novel Language-Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) We identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) We show that LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Code will be made available here.

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

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Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation

Extracting Motion and Appearance via Inter-Frame Attention f...

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

作者： Zhang, Guozhen Zhu, Yuhan Wang, Haonan Chen, Youxin Wu, Gangshan Wang, Limin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Shanghai AI Lab Shanghai Peoples R China Samsung Elect China R&D Ctr Beijing Peoples R China

ISBN: (纸本)9798350301298

Effectively extracting inter-frame motion and appearance information is important for video frame interpolation (VFI). Previous works either extract both types of information in a mixed way or devise separate modules for each type of information, which lead to representation ambiguity and low efficiency. In this paper, we propose a new module to explicitly extract motion and appearance information via a unified operation. Specifically, we rethink the information process in inter-frame attention and reuse its attention map for both appearance feature enhancement and motion information extraction. Furthermore, for efficient VFI, our proposed module could be seamlessly integrated into a hybrid CNN and Transformer architecture. This hybrid pipeline can alleviate the computational complexity of inter-frame attention as well as preserve detailed low-level structure information. Experimental results demonstrate that, for both fixed- and arbitrary-timestep interpolation, our method achieves state-of-the-art performance on various datasets. Meanwhile, our approach enjoys a lighter computation overhead over models with close performance. The source code and models are available at https://***/MCG-NJU/EMA-VFI.

关键词： Low-level vision

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Open-set Fine-grained Retrieval via Prompting vision-Language Evaluator

Open-set Fine-grained Retrieval via Prompting Vision-Languag...

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

作者： Wang, Shijie Chang, Jianlong Li, Haojie Wang, Zhihui Ouyang, Wanli Tian, Qi Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Huawei Cloud & AI Shenzhen Peoples R China Shandong Univ Sci & Technol Coll Comp & Engn Qingdao Peoples R China Univ Sydney Sense Time Comp Vis Res Grp Sydney NSW Australia

ISBN: (纸本)9798350301298

Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to retrieve unknown subcategories during evaluation. However, current works focus on close-set visual concepts, where all the subcategories are pre-defined, and make it hard to capture discriminative knowledge from unknown subcategories, consequently failing to handle unknown subcategories in open-world scenarios. In this work, we propose a novel Prompting vision-Language Evaluator (PLEor) framework based on the recently introduced contrastive language-image pretraining (CLIP) model, for open-set fine-grained retrieval. PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios. To make pre-trained CLIP model sensitive to category-specific discrepancies, we design a dual prompt scheme to learn a vision prompt specifying the category-specific discrepancies, and turn random vectors with category names in a text prompt into category-specific discrepancy descriptions. Moreover, a vision-language evaluator is proposed to semantically align the vision and text prompts based on CLIP model, and reinforce each other. In addition, we propose an open-set knowledge transfer to transfer the category-specific discrepancies into the backbone network using knowledge distillation mechanism. Quantitative and qualitative experiments show that our PLEor achieves promising performance on open-set fine-grained datasets.

关键词： vision applications and systems

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Masked Autoencoders Enable Efficient Knowledge Distillers

Masked Autoencoders Enable Efficient Knowledge Distillers

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

作者： Bai, Yutong Wang, Zeyu Xiao, Junfei Wei, Chen Wang, Huiyu Yuille, Alan Zhou, Yuyin Xie, Cihang Johns Hopkins Univ Baltimore MD 21218 USA Univ Calif Santa Cruz Santa Cruz CA USA

ISBN: (纸本)9798350301298

This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders. Our approach is simple: in addition to optimizing the pixel reconstruction loss on masked inputs, we minimize the distance between the intermediate feature map of the teacher model and that of the student model. This design leads to a computationally efficient knowledge distillation framework, given 1) only a small visible subset of patches is used, and 2) the (cumbersome) teacher model only needs to be partially executed, i.e., forward propagate inputs through the first few layers, for obtaining intermediate feature maps. Compared to directly distilling fine-tuned models, distilling pre-trained models substantially improves downstream performance. For example, by distilling the knowledge from an MAE pre-trained ViT-L into a ViT-B, our method achieves 84.0% ImageNet top-1 accuracy, outperforming the baseline of directly distilling a fine-tuned ViT-L by 1.2%. More intriguingly, our method can robustly distill knowledge from teacher models even with extremely high masking ratios: e.g., with 95% masking ratio where merely TEN patches are visible during distillation, our ViT-B competitively attains a top-1 ImageNet accuracy of 83.6%;surprisingly, it can still secure 82.4% top-1 ImageNet accuracy by aggressively training with just FOUR visible patches (98% masking ratio). The code and models are publicly available at https://***/UCSC-VLAA/DMAE.

关键词： Efficient and scalable vision

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Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects

Unknown Sniffer for Object Detection: Don't Turn a Blind Eye...

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

作者： Liang, Wenteng Xue, Feng Liu, Yihao Zhong, Guofeng Ming, Anlong Beijing Univ Posts & Telecommunicat Beijing Peoples R China Chongqing Univ Posts & Telecommunicat Chongqing Peoples R China

ISBN: (纸本)9798350301298

The recently proposed open-world object and open-set detection have achieved a breakthrough in finding never-seen-before objects and distinguishing them from known ones. However, their studies on knowledge transfer from known classes to unknown ones are not deep enough, resulting in the scanty capability for detecting unknowns hidden in the background. In this paper, we propose the unknown sniffer (UnSniffer) to find both unknown and known objects. Firstly, the generalized object confidence (GOC) score is introduced, which only uses known samples for supervision and avoids improper suppression of unknowns in the background. Significantly, such confidence score learned from known objects can be generalized to unknown ones. Additionally, we propose a negative energy suppression loss to further suppress the non-object samples in the background. Next, the best box of each unknown is hard to obtain during inference due to lacking their semantic information in training. To solve this issue, we introduce a graph-based determination scheme to replace hand-designed non-maximum suppression (NMS) post-processing. Finally, we present the Unknown Object Detection Benchmark, the first publicly benchmark that encompasses precision evaluation for unknown detection to our knowledge. Experiments show that our method is far better than the existing state-of-the-art methods. Code is available at: https://***/Went-Liang/UnSniffer.

关键词： detection recognition: Categorization retrieval

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