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TransLoc4D: Transformer-based 4D Radar Place recognition

TransLoc4D: Transformer-based 4D Radar Place Recognition

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

作者： Peng, Guohao Li, Heshan Zhao, Yangyang Zhang, Jun Wu, Zhenyu Zheng, Pengyu Wang, Danwei Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350353006

Place recognition is crucial for unmanned vehicles in terms of localization and mapping. Recent years have witnessed numerous explorations in the field, where 2D cameras and 3D LiDARs are mostly employed. Despite their admirable performance, they may encounter challenges in adverse weather such as rain and fog. Hopefully, 4D millimeter-wave radar emerges as a promising alternative, as its longer wavelength makes it virtually immune to interference from tiny particles of fog and rain. Therefore, in this work, we propose a novel 4D radar place recognition model, TransLoc4D, based on sparse convolutions and Transformer structures. Specifically, a MinkLoc4D backbone is first proposed to leverage the multi-modal information from 4D radar scans. Rather than merely capturing geometric structures of point clouds, MinkLoc4D additionally explores their intensity and velocity properties. After feature extraction, a Transformer layer is introduced to enhance local features before aggregation, where linear self-attention captures the long-range dependencies of the point cloud, alleviating its sparsity and noise. To validate TransLoc4D, we construct two datasets and set up benchmarks for 4D radar place recognition. Experiments validate the feasibility of TransLoc4D and demonstrate it can robustly deal with dynamic and adverse environments.

关键词： localization Place recognition Scene Understanding SLAM

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Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection

Consistency Learning via Decoding Path Augmentation for Tran...

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

作者： Park, Jihwan Lee, SeungJun Heo, Hwan Choi, Hyeong Kyu Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea Kakao Brain Seongnam South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-Object Interaction detection is a holistic visual recognition task that entails object detection as well as interaction classification. Previous works of HOI detection has been addressed by the various compositions of sub- set predictions, e.g., Image -> HO -> I, Image -> HI -> O. Recently, transformer based architecture for HOI has emerged, which directly predicts the HOI triplets in an end- to-end fashion (Image -> HOI). Motivated by various inference paths for HOI detection, we propose cross path consistency learning (CPC), which is a novel end-to-end learning strategy to improve HOI detection for transformers by leveraging augmented decoding paths. CPC learning enforces all the possible predictions from permuted inference sequences to be consistent. This simple scheme makes the model learn consistent representations, thereby improving generalization without increasing model capacity. Our experiments demonstrate the effectiveness of our method, and we achieved significant improvement on V-COCO and HICO-DET compared to the baseline models. Our code is available at https://***/mlvlab/CPChoi.

关键词： Visualization computer vision Codes Computational modeling computer architecture Object detection Transformers

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Tactical Rewind: Self-Correction via Backtracking in vision-and-Language Navigation 32

Tactical Rewind: Self-Correction via Backtracking in Vision-...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ke, Liyiming Li, Xiujun Bisk, Yonatan Holtzman, Ari Gan, Zhe Liu, Jingjing Gao, Jianfeng Choi, Yejin Srinivasa, Siddhartha Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA Microsoft Res AI Washington DC USA Allen Inst Artificial Intelligence Seattle WA USA

ISBN: (纸本)9781728132938

We present the Frontier Aware Search with backTracking (FAST) Navigator,a general framework for action decoding, that achieves state-of-the-art results on the Room-to-Room (R2R) vision-and-Language navigation challenge of Anderson et. al. (2018). Given a natural language instruction and photo-realistic image views of a previously unseen environment, the agent was tasked with navigating from source to target location as quickly as possible. While all current approaches make local action decisions or score entire trajectories using beam search, ours balances local and global signals when exploring an unobserved environment. Importantly, this lets us act greedily but use global signals to backtrack when necessary. Applying FAST framework to existing state-of-the-art models achieved a 17% relative gain, an absolute 6% gain on Success rate weighted by Path Length (SPL).

关键词： Robotics + Driving vision + Language

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Transferable Adversarial Attacks on vision Transformers with Token Gradient Regularization

Transferable Adversarial Attacks on Vision Transformers with...

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

作者： Zhang, Jianping Huang, Yizhan Wu, Weibin Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China

ISBN: (纸本)9798350301298

vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security threat to ViT-based applications. Therefore, it is vital to design effective transfer-based attacks to identify the deficiencies of ViTs beforehand in security-sensitive scenarios. Existing efforts generally focus on regularizing the input gradients to stabilize the updated direction of adversarial samples. However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima. To overcome the shortcomings of existing approaches, we propose the Token Gradient Regularization (TGR) method. According to the structural characteristics of ViTs, TGR reduces the variance of the back-propagated gradient in each internal block of ViTs in a token-wise manner and utilizes the regularized gradient to generate adversarial samples. Extensive experiments on attacking both ViTs and CNNs confirm the superiority of our approach. Notably, compared to the state-of-the-art transfer-based attacks, our TGR offers a performance improvement of 8.8% on average.

关键词： Adversarial attack and defense

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Adaptive Image Transformer for One-Shot Object Detection

Adaptive Image Transformer for One-Shot Object Detection

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

作者： Chen, Ding-Jie Hsieh, He-Yen Liu, Tyng-Luh Acad Sinica Inst Informat Sci Taipei Taiwan Taiwan AI Labs Taipei Taiwan

ISBN: (纸本)9781665445092

One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch. The main difficulty lies in the situation that the class label of the query patch and its respective examples are not available in the training data. Our main idea leverages the concept of language translation to boost metric-learning-based detection methods. Specifically, we emulate the language translation process to adaptively translate the feature of each object proposal to better correlate the given query feature for discriminating the class-similarity among the proposal-query pairs. To this end, we propose the Adaptive Image Transformer (AIT) module that deploys an attention-based encoder-decoder architecture to simultaneously explore intra-coder and inter-coder (i.e., each proposal-query pair) attention. The adaptive nature of our design turns out to be flexible and effective in addressing the one-shot learning scenario. With the informative attention cues, the proposed model excels in predicting the class-similarity between the target image proposals and the query image patch. Though conceptually simple, our model significantly outperforms a state-of-the-art technique, improving the unseenclass object classification from 63.8 mAP and 22.0 AP50 to 72.2 mAP and 24.3 AP50 on the PASCAL-VOC and MS-COCO benchmark datasets, respectively.

关键词： Adaptation models Training data Object detection Predictive models Feature extraction Transformers pattern recognition

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COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction

COMPLETER: Incomplete Multi-view Clustering via Contrastive ...

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

作者： Lin, Yijie Gou, Yuanbiao Liu, Zitao Li, Boyun Lv, Jiancheng Peng, Xi Sichuan Univ Coll Comp Sci Chengdu Peoples R China TAL Educ Grp Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data. To this end, we propose a novel objective that incorporates representation learning and data recovery into a unified framework from the view of information theory. To be specific, the informative and consistent representation is learned by maximizing the mutual information across different views through contrastive learning, and the missing views are recovered by minimizing the conditional entropy of different views through dual prediction. To the best of our knowledge, this could be the first work to provide a theoretical framework that unifies the consistent representation learning and cross-view data recovery. Extensive experimental results show the proposed method remarkably outperforms 10 competitive multi-view clustering methods on four challenging datasets. The code is available at https://***.

关键词： computer vision Codes Clustering methods Entropy Task analysis Mutual information

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Exploring Effective Data for Surrogate Training Towards Black-box Attack

Exploring Effective Data for Surrogate Training Towards Blac...

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

作者： Sun, Xuxiang Cheng, Gong Li, Hongda Pei, Lei Han, Junwei Northwestern Polytech Univ Sch Automat Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Without access to the training data where a black-box victim model is deployed, training a surrogate model for black-box adversarial attack is still a struggle. In terms of data, we mainly identify three key measures for effective surrogate training in this paper. First, we show that leveraging the loss introduced in this paper to enlarge the inter-class similarity makes more sense than enlarging the inter-class diversity like existing methods. Next, unlike the approaches that expand the intra-class diversity in an implicit model-agnostic fashion, we propose a loss function specific to the surrogate model for our generator to enhance the intraclass diversity. Finally, in accordance with the in-depth observations for the methods based on proxy data, we argue that leveraging the proxy data is still an effective way for surrogate training. To this end, we propose a triple-player framework by introducing a discriminator into the traditional data-free framework. In this way, our method can be competitive when there are few semantic overlaps between the scarce proxy data (with the size between 1k and 5k) and the training data. We evaluate our method on a range of victim models and datasets. The extensive results witness the effectiveness of our method. Our source code is available at https://***/ST-Data.

关键词： Training computer vision Codes Computational modeling Semantics Training data Diversity methods

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SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization

SWEM: Towards Real-Time Video Object Segmentation with Seque...

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

作者： Lin, Zhihui Yang, Tianyu Li, Maomao Wang, Ziyu Yuan, Chun Jiang, Wenhao Liu, Wei Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tencent AI Lab Shenzhen Peoples R China Tencent Data Platform Shenzhen Peoples R China Tsinghua Shenzhen Int Grad Sch Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS). However, continuously growing and redundant template features lead to an inefficient inference. To alleviate this, we propose a novel Sequential Weighted Expectation-Maximization (SWEM) network to greatly reduce the redundancy of memory features. Different from the previous methods which only detect feature redundancy between frames, SWEM merges both intra-frame and inter-frame similar features by leveraging the sequential weighted EM algorithm. Further, adaptive weights for frame features endow SWEM with the flexibility to represent hard samples, improving the discrimination of templates. Besides, the proposed method maintains a fixed number of template features in memory, which ensures the stable inference complexity of the VOS system. Extensive experiments on commonly used DAVIS and YouTube-VOS datasets verify the high efficiency (36 FPS) and high performance (84.3% JSzT on DAVIS 2017 validation dataset) of SWEM.

关键词： computer vision Shape Computational modeling Redundancy Object segmentation Streaming media Feature extraction

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Neural Side-By-Side: Predicting Human Preferences for No-Reference Super-Resolution Evaluation

Neural Side-By-Side: Predicting Human Preferences for No-Ref...

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

作者： Khrulkov, Valentin Babenko, Artem Yandex Moscow Russia Natl Res Univ Higher Sch Econ Moscow Russia

ISBN: (纸本)9781665445092

Super-resolution based on deep convolutional networks is currently gaining much attention from both academia and industry. However, lack of proper evaluation measures makes it difficult to compare approaches, hampering progress in the field. Traditional measures, such as PSNR or SSIM, are known to poorly correlate with the human perception of image quality. Therefore, in existing works common practice is also to report Mean-Opinion-Score (MOS) - the results of human evaluation of super-resolved images. Unfortunately, the MOS values from different papers are not directly comparable, due to the varying number of raters, their subjectivity, etc. By this paper, we introduce Neural Side-By-Side - a new measure that allows super-resolution models to be compared automatically, effectively approximating human preferences. Namely, we collect a large dataset of aligned image pairs, which were produced by different super-resolution models. Then each pair is annotated by several raters, who were instructed to choose a more visually appealing image. Given the dataset and the labels, we trained a CNN model that obtains a pair of images and for each image predicts a probability of being more preferable than its counterpart. In this work, we show that Neural Side-By-Side generalizes across both new models and new data. Hence, it can serve as a natural approximation of human preferences, which can be used to compare models or tune hyperparameters 'without raters' assistance. We open-source the dataset and the pretrained model' and expect that it will become a handy tool for researchers and practitioners.

关键词： Industries Image quality computer vision Computational modeling Superresolution Tools Predictive models

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A Minimal Solution to the Generalized Pose-and-Scale Problem 27

A Minimal Solution to the Generalized Pose-and-Scale Problem

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ventura, Jonathan Arth, Clemens Reitmayr, Gerhard Schmalstieg, Dieter Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria Qualcomm Austria Res Ctr Vienna Austria

ISBN: (纸本)9781479951178

We propose a novel solution to the generalized camera pose problem which includes the internal scale of the generalized camera as an unknown parameter. This further generalization of the well-known absolute camera pose problem has applications in multi-frame loop closure. While a well-calibrated camera rig has a fixed and known scale, camera trajectories produced by monocular motion estimation necessarily lack a scale estimate. Thus, when performing loop closure in monocular visual odometry, or registering separate structure-from-motion reconstructions, we must estimate a seven degree-of-freedom similarity transform from corresponding observations. Existing approaches solve this problem, in specialized configurations, by aligning 3D triangulated points or individual camera pose estimates. Our approach handles general configurations of rays and points and directly estimates the full similarity transformation from the 2D-3D correspondences. Four correspondences are needed in the minimal case, which has eight possible solutions. The minimal solver can be used in a hypothesize-and-test architecture for robust transformation estimation. Our solver also produces a least-squares estimate in the overdetermined case. The approach is evaluated experimentally on synthetic and real datasets, and is shown to produce higher accuracy solutions to multi-frame loop closure than existing approaches.

关键词： 3d computer vision absolute pose generalized camera loop closure minimal solvers structure from motion

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