Visual place recognition is a challenging task for applications such as autonomous driving navigation and mobile robot localization. Distracting elements presenting in complex scenes often lead to deviations in the perception of visual place. To address this problem, it is crucial to integrate information from only task-relevant regions into image representations. In this paper, we introduce a novel holistic place recognition model, TransVPR, based on vision Transformers. It benefits from the desirable property of the self-attention operation in Transformers which can naturally aggregate task-relevant features. Attentions from multiple levels of the Transformer, which focus on different regions of interest, are further combined to generate a global image representation. In addition, the output tokens from Transformer layers filtered by the fused attention mask are considered as key-patch descriptors, which are used to perform spatial matching to re-rank the candidates retrieved by the global image features. The whole model allows end-to-end training with a single objective and image-level supervision. TransVPR achieves state-of-the-art performance on several real-world benchmarks while maintaining low computational time and storage requirements.
Visual Place Recognition (VPR) is a crucial part of mobile robotics and autonomous driving as well as other computer vision tasks. It refers to the process of identifying a place depicted in a query image using only computer vision. At large scale, repetitive structures, weather and illumination changes pose a real challenge, as appearances can drastically change over time. Along with tackling these challenges, an efficient VPR technique must also be practical in real-world scenarios where latency matters. To address this, we introduce MixVPR, a new holistic feature aggregation technique that …
Shenzhen University（深圳大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Australian National University（澳大利亚国立大学）
Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spati…
SuperPlace: The Renaissance of Classical Feature Aggregation for Visual Place Recognition in the Era of Foundation Models
Southern University of Science and Technology（南方科技大学）；Peng Cheng Laboratory（鹏城实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Cambridge University（剑桥大学）
DSFormer: A Dual-Scale Cross-Learning Transformer for Visual Place Recognition
Multi-agent Robot Research Center, Department of Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院多智能体机器人研究中心）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Soochow University（苏州大学）
Towards Implicit Aggregation: Robust Image Representation for Place Recognition in the Transformer Era
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Pengcheng Laboratory（鹏城实验室）；Shenyang Institute of Automation, Chinese Academy of Sciences（沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））
In this paper, we present a solution to Large-Scale Video Classification Challenge (LSVC2017) [1] that ranked the 1st place. We focused on a variety of modalities that cover visual, motion and audio. Also, we visualized the aggregation process to better understand how each modality takes effect. Among the extracted modalities, we found Temporal-Spatial features calculated by 3D convolution quite promising that greatly improved the performance. We attained the official metric mAP 0.8741 on the testing set with the ensemble model.
AANet: Aggregation and Alignment Network with Semi-hard Positive Sample Mining for Hierarchical Place Recognition
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；Central South University（中南大学）
Semantic segmentation is an important task in computer vision, from which some important usage scenarios are derived, such as autonomous driving, scene parsing, etc. Due to the emphasis on the task of video semantic segmentation, we participated in this competition. In this report, we briefly introduce the solutions of team 'BetterThing' for the ICCV2021 - Video Scene Parsing in the Wild Challenge. Transformer is used as the backbone for extracting video frame features, and the final result is the aggregation of the output of two Transformer models, SWIN and VOLO. This solution achieves 57.3% …
Beyond First-Order: Learning Riemannian Geometries for Invariant Visual Place Recognition
The Hong Kong University of Science and Technology, Hong Kong, China（香港科技大学）；University of Macau, Macau, China（澳门大学）；University of Science and Technology Beijing, Beijing, China（北京科技大学）
This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this paper, we introduce a strong backbone which is cross-window based Shuffle Transformer for presenting accurate face parsing representation. To further obtain the finer segmentation results, especially on the edges, we introduce a Feature Alignment Aggregation (FAA) module. It can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Benefiting from the stronge…
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河畔临床医院）；University of Dundee（邓迪大学）
Segmentation of both large and small white matter hyperintensities/lesions in brain MR images is a challenging task which has drawn much attention in recent years. We propose a multi-scale aggregation model framework to deal with volume-varied lesions. Firstly, we present a specifically-designed network for small lesion segmentation called Stack-Net, in which multiple convolutional layers are connected, aiming to preserve rich local spatial information of small lesions before the sub-sampling layer. Secondly, we aggregate multi-scale Stack-Nets with different receptive fields to learn multi-sc…
SAGE: Spatial-visual Adaptive Graph Exploration for Efficient Visual Place Recognition
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology（教育部计算功率网络与信息安全部、山东计算机科学中心、齐鲁大学）
The paper presents a simple and effective learning-based method for computing a discriminative 3D point cloud descriptor for place recognition purposes. Recent state-of-the-art methods have relatively complex architectures such as multi-scale oyramid of point Transformers combined with a pyramid of feature aggregation modules. Our method uses a simple and efficient 3D convolutional feature extraction, based on a sparse voxelized representation, enhanced with channel attention blocks. We employ recent advances in image retrieval and propose a modified version of a loss function based on a diffe…
Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization
Zhejiang University（浙江大学）；Key Laboratory for Corneal Diseases Research of Zhejiang Province（浙江省角膜病研究重点实验室）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Shanghai AI Laboratory（上海人工智能实验室）
Huazhong University of Science and Technology（华中科技大学）；SenseTime Research（商汤科技研究院）；Shanghai AI Laboratory（上海人工智能实验室）
Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as w…
Nanjing University of Science and Technology（南京理工大学）；University of Macau（澳门大学）
Place recognition or loop closure detection is one of the core components in a full SLAM system. In this paper, aiming at strengthening the relevancy of local neighboring points and the contextual dependency among global points simultaneously, we investigate the exploitation of transformer-based network for feature extraction, and propose a Hierarchical Transformer for Place Recognition (HiTPR). The HiTPR consists of four major parts: point cell generation, short-range transformer (SRT), long-range transformer (LRT) and global descriptor aggregation. Specifically, the point cloud is initially …
The Chinese University of Hong Kong（香港中文大学）；Carnegie Mellon University（卡内基梅隆大学）；Shanghai Jiao Tong University（上海交通大学）
Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dynamic environments. In this paper, we develop a novel deep neural network, named LPD-Net (Large-scale Place Description Network), which can extract discriminative and generalizable global descriptors from the raw 3D point cloud. Two modules, the adaptive local feature extraction module and the graph-based neighborhood aggregation module, are proposed, which contribute to extract …
EDTformer: An Efficient Decoder Transformer for Visual Place Recognition
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences（中国科学院光电信息处理重点实验室）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Online 3D Multi-Camera Perception through Robust 2D Tracking and Depth-based Late Aggregation
Optoelectronics Center, Viettel Aerospace Institute, Viettel Group（Viettel集团光学电子中心、 Viettel航空航天研究所）；University of Engineering and Technology, Vietnam National University（越南国家大学工程大学）；School of Electrical and Electronic Engineering, Hanoi University of Science and Technology（河内科学技术大学电子与电气工程学院）
This paper aims to investigate representation learning for large scale visual place recognition, which consists of determining the location depicted in a query image by referring to a database of reference images. This is a challenging task due to the large-scale environmental changes that can occur over time (i.e., weather, illumination, season, traffic, occlusion). Progress is currently challenged by the lack of large databases with accurate ground truth. To address this challenge, we introduce GSV-Cities, a new image dataset providing the widest geographic coverage to date with highly accur…
Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans. However, unsupervised networks have long lagged behind the performance of their supervised counterparts, especially in the domain of large-scale visual recognition. Recent developments in training deep convolutional embeddings to maximize non-parametric instan…
Queensland University of Technology（昆士兰科技大学）；QUT Centre for Robotics（QUT机器人中心）
Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world. This paper introduces Patch-NetVLAD, which provides a novel formulation for combining the advantages of both local and global descriptor methods by deriving patch-level features from NetVLAD residuals. Unlike the fixed spatial neighborhood regime of existing local keypoint features, our method enables aggregation and matching of deep-learned local features defined over the feature-space grid. We further introd…
Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition
School of Electronics and Computer Science, University of Southampton（苏塞克斯大学电子与计算机科学学院）；Department of Computer Science and Engineering, Hanyang University（翰阳大学计算机科学与工程系）；QUT Centre for Robotics, School of Electrical Engineering and Robotics（昆士兰大学机器人中心、电气工程与机器人学院）；School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）
MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions
Institute for Engineering Research (I3E)（工程研究所（I3E））；Valencian Graduate School and Research Network for Artificial Intelligence（瓦伦西亚人工智能研究生院及研究网络）；Univ of Coimbra（科英布拉大学）
First Place Solution to the CVPR'2023 AQTC Challenge: A Function-Interaction Centric Approach with Spatiotemporal Visual-Language Alignment
Hunan University（湖南大学）；Quanzhou Institute of Industrial Design and Machine Intelligence Innovation, Hunan University（湖南大学泉州工业设计与机器智能创新研究院）；University of Central Florida（中佛罗里达大学）
Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbol…
A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is commonly treated as an image retrieval task, where a query image from an unknown location is matched with relevant instances from geo-tagged gallery database. Different from conventional VPR settings where the query images and gallery images come from the same domain, we propose a more common but challenging setup where the query images are collected under a new unseen condition. The two domains involved in this work are contemporary…
Region Matters: Efficient and Reliable Region-Aware Visual Place Recognition
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology（教育部计算功率网络与信息安全部，山东计算机科学中心，齐鲁大学技术学院）；Spatialtemporal AI（时空AI）；School of Architecture and Urban Planning, Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen University（建筑与城市规划学院，粤港澳大湾区智慧城市联合实验室，深圳大学）
QUT Centre for Robotics（昆士兰科技大学机器人中心）；School of Electrical Engineering and Robotics, QUT（昆士兰科技大学电气工程与机器人学院）；Queensland University of Technology (QUT)（昆士兰科技大学）
Visual Place Recognition (VPR) is the task of matching current visual imagery from a camera to images stored in a reference map of the environment. While initial VPR systems used simple direct image methods or hand-crafted visual features, recent work has focused on learning more powerful visual features and further improving performance through either some form of sequential matcher / filter or a hierarchical matching process. In both cases the performance of the initial single-image based system is still far from perfect, putting significant pressure on the sequence matching or (in the case …
University of Science and Technology of China（中国科学技术大学）
Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achieved great progress. However, It is still a challenge to restore the real-world LR image with complicated authentic degradations. Therefore, we propose FAN, a frequency aggregation network, to address the real-world image super-resolu-tion problem. Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output correspon…
