University of Oxford（牛津大学）；VGG（视觉几何组）；Dept. of Eng. Science（工程科学系）
Olivia Wiles, Sebastien Ehrhardt, Andrew Zisserman
备注：
Accepted at CVPR 2021. Project page: https://www.robots.ox.ac.uk/~ow/coam.html. Formerly D2D: Learning to find good correspondences for image matching and manipulation
We propose a new approach to determine correspondences between image pairs in the wild under large changes in illumination, viewpoint, context, and material. While other approaches find correspondences between pairs of images by treating the images independently, we instead condition on both images to implicitly take account of the differences between them. To achieve this, we introduce (i) a spatial attention mechanism (a co-attention module, CoAM) for conditioning the learned features on both images, and (ii) a distinctiveness score used to choose the best matches at test time. CoAM can be added to standard architectures and trained using self-supervision or supervised data, and achieves a significant performance improvement under hard conditions, e.g. large viewpoint changes. We demonstrate that models using CoAM achieve state of the art or competitive results on a wide range of tasks: local matching, camera localization, 3D reconstruction, and image stylization.
This paper introduces Attentive Implicit Representation Networks (AIR-Nets), a simple, but highly effective architecture for 3D reconstruction from point clouds. Since representing 3D shapes in a local and modular fashion increases generalization and reconstruction quality, AIR-Nets encode an input point cloud into a set of local latent vectors anchored in 3D space, which locally describe the object's geometry, as well as a global latent description, enforcing global consistency. Our model is the first grid-free, encoder-based approach that locally describes an implicit function. The vector at…
University of California, Santa Barbara（加州大学圣巴巴拉分校）；IBM T. J. Watson Research Centre（IBM托马斯·J·沃森研究中心）
While observing complex events with multiple actors, humans do not assess each actor separately, but infer from the context. The surrounding context provides essential information for understanding actions. To this end, we propose to replace region of interest(RoI) pooling with an attention module, which ranks each spatio-temporal region's relevance to a detected actor instead of cropping. We refer to these as Actor-Conditioned Attention Maps (ACAM), which amplify/dampen the features extracted from the entire scene. The resulting actor-conditioned features focus the model on regions that are r…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events, like collisions and casualties. However, existing driver attention prediction models neglect the distraction state and intention of the driver, which can significantly influence how they observe their surroundings. To address these issues, we present a new driver attention dataset, CoCAtt (Cognitive-Conditioned Attention). Unlike previous driver attentio…
Text-guided image manipulation tasks have recently gained attention in the vision-and-language community. While most of the prior studies focused on single-turn manipulation, our goal in this paper is to address the more challenging multi-turn image manipulation (MTIM) task. Previous models for this task successfully generate images iteratively, given a sequence of instructions and a previously generated image. However, this approach suffers from under-generation and a lack of generated quality of the objects that are described in the instructions, which consequently degrades the overall perfo…
Can We Build Scene Graphs, Not Classify Them? FlowSG: Progressive Image-Conditioned Scene Graph Generation with Flow Matching
The Laboratory of Intelligent Collaborative Computing of UESTC（UESTC智能协同计算实验室）；Tianfu Jiangxi Laboratory（天斧江西实验室）；Monash University（墨尔本大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））
Tel Aviv University（特拉维夫大学）；Technion（以色列理工学院）；NetApp
We present a method for matching a text sentence from a given corpus to a given video clip and vice versa. Traditionally video and text matching is done by learning a shared embedding space and the encoding of one modality is independent of the other. In this work, we encode the dataset data in a way that takes into account the query's relevant information. The power of the method is demonstrated to arise from pooling the interaction data between words and frames. Since the encoding of the video clip depends on the sentence compared to it, the representation needs to be recomputed for each pot…
Univ Lyon, INSA-Lyon, CNRS, LIRIS（里昂大学，里昂国立应用科学学院，法国国家科学研究中心，里昂信息科学与技术实验室）；Laboratoire d’Informatique de l’Université de Tours (EA 6300), INSA Centre Val de Loire（图尔大学信息学实验室，卢瓦尔河谷国立应用科学学院）
We address human action recognition from multi-modal video data involving articulated pose and RGB frames and propose a two-stream approach. The pose stream is processed with a convolutional model taking as input a 3D tensor holding data from a sub-sequence. A specific joint ordering, which respects the topology of the human body, ensures that different convolutional layers correspond to meaningful levels of abstraction. The raw RGB stream is handled by a spatio-temporal soft-attention mechanism conditioned on features from the pose network. An LSTM network receives input from a set of image l…
Recently Transformers have provided state-of-the-art performance in sparse matching, crucial to realize high-performance 3D vision applications. Yet, these Transformers lack efficiency due to the quadratic computational complexity of their attention mechanism. To solve this problem, we employ an efficient linear attention for the linear computational complexity. Then, we propose a new attentional aggregation that achieves high accuracy by aggregating both the global and local information from sparse keypoints. To further improve the efficiency, we propose the joint learning of feature matching…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events, like collisions and casualties. However, existing driver attention prediction models neglect the distraction state and intention of the driver, which can significantly influence how they observe their surroundings. To address these issues, we present a new driver attention dataset, CoCAtt (Cognitive-Conditioned Attention). Unlike previous driver attentio…
Huazhong University of Science & Technology（华中科技大学）；Wuhan National Laboratory for Optoelectronics（武汉国家光电实验室）
Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. To generate reliable attention weights, we propose multi-level adaptive patch matching to improve the distinctiveness of the matching cost at different disparities ev…
Textual-visual matching aims at measuring similarities between sentence descriptions and images. Most existing methods tackle this problem without effectively utilizing identity-level annotations. In this paper, we propose an identity-aware two-stage framework for the textual-visual matching problem. Our stage-1 CNN-LSTM network learns to embed cross-modal features with a novel Cross-Modal Cross-Entropy (CMCE) loss. The stage-1 network is able to efficiently screen easy incorrect matchings and also provide initial training point for the stage-2 training. The stage-2 CNN-LSTM network refines th…
We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language. DANs attend to specific regions in images and words in text through multiple steps and gather essential information from both modalities. Based on this framework, we introduce two types of DANs for multimodal reasoning and matching, respectively. The reasoning model allows visual and textual attentions to steer each other during collaborative inference, which is useful for tasks such as Visual Question Answering (VQA). In additi…
SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation
Sydney AI Centre, The University of Sydney（悉尼人工智能中心，悉尼大学）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Australian Artificial Intelligence Institute, University of Technology Sydney（澳大利亚人工智能研究所，技术大学悉尼）；Department of Information Systems, City University of Hong Kong（香港城市大学信息系统学院）
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching
Chonnam National University（全南大学）；AISEED, Inc.（AISEED公司）；Ho Chi Minh University of Science（胡志明市科学大学）；MIREA – Russian Technological University（俄罗斯国立研究型技术大学（莫斯科国立精密机械与计算机科学研究所））
In recent years, powered by the learned discriminative representation via graph neural network (GNN) models, deep graph matching methods have made great progresses in the task of matching semantic features. However, these methods usually rely on heuristically generated graph patterns, which may introduce unreliable relationships to hurt the matching performance. In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching. GLAM adopts a pure attention-based framework for both graph learning and graph …
SCMA: Structure-Conditioned and Metal-Aware Flow Matching for CT Metal Artifact Reduction
School of Mathematical Sciences, Capital Normal University（首都师范大学数学科学学院）；College of Mathematics Science, Inner Mongolia Normal University（内蒙古师范大学数学科学学院）；National Center for Applied Mathematics Beijing, Capital Normal University（首都师范大学北京应用数学中心）；Academy for Multidisciplinary Studies, Capital Normal University（首都师范大学交叉科学研究院）
MTFlow: Time-Conditioned Flow Matching for Microtubule Segmentation in Noisy Microscopy Images
CNRS, Univ. Rennes, Institute of Genetics and Development of Rennes (IGDR), Rennes, France（法国里昂大学CNRS研究所、里昂大学基因与发育研究所）；TIAD Laboratory, Sciences and Technology Faculty, Sultan Moulay Slimane Univ.（摩洛哥苏丹穆莱·西拉曼大学科技学院TIAD实验室）
Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-grained relevance across different modalities. However, the cross-modal attention models of existing methods could be sub-optimal and inaccurate because there is no direct supervision provided during the training process. In this work, we propose two novel training strategies, namely Contrastive Content Re-sourcing (CCR) and Contrastive Content Swapping (CCS) constraints, to address such limitations. These constraints supe…
Optical flow estimation is a fundamental task in computer vision. Recent direct-regression methods using deep neural networks achieve remarkable performance improvement. However, they do not explicitly capture long-term motion correspondences and thus cannot handle large motions effectively. In this paper, inspired by the traditional matching-optimization methods where matching is introduced to handle large displacements before energy-based optimizations, we introduce a simple but effective global matching step before the direct regression and develop a learning-based matching-optimization fra…
Beihang University（北京航空航天大学）；Zhejiang University of Technology（浙江工业大学）；Institute for Infocomm Research（信息通信研究院）
Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on. Both handcrafted-based and recently emerged deep learning-based keypoints matching methods merely rely on keypoints and local features, while losing sight of other available sensors such as inertial measurement unit (IMU) in the above applications. In this paper, we demonstrate that the motion estimation from IMU integration can be used to exploit the spatial distribution prior of keypoints between images. To this end, a probabi…
Southeast University（东南大学）；Nanjing University of Posts and Telecommunication（南京邮电大学）
Heavy computation is a bottleneck limiting deep-learningbased feature matching algorithms to be applied in many realtime applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks, since sparse keypoint based descriptors are expected to be matched. This paper tackles this problem and proposes two concepts: 1) a novel parallel attention model entitled ParaFormer and 2) a graph based U-Net architecture with attentional pooling. First, ParaFormer fuses features and keypoint positions through the concept of amplitude and phase,…
Nanjing University of Information Science and Technology（南京信息工程大学）；East China Normal University（华东师范大学）
Image-text matching is an important multi-modal task with massive applications. It tries to match the image and the text with similar semantic information. Existing approaches do not explicitly transform the different modalities into a common space. Meanwhile, the attention mechanism which is widely used in image-text matching models does not have supervision. We propose a novel attention scheme which projects the image and text embedding into a common space and optimises the attention weights directly towards the evaluation metrics. The proposed attention scheme can be considered as a kind of…
Accelerating Multimodal Large Language Models with Prior-Corrected Token Reduction
School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology（浙江省数字化服务技术重点实验室）；Hong Kong Polytechnic University（香港理工大学）；The University of Southern Queensland（南昆士兰大学）
Shanghai Jiao Tong University（上海交通大学）；Visbody Inc（维塑科技有限公司）；University of California, Merced（加州大学默塞德分校）；Google Inc（谷歌公司）；Northwestern Polytechnical University（西北工业大学）；Massachusetts Institute of Technology（麻省理工学院）
In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets. Specifically, for applying single object tracking in MOT, we introduce a cost-sensitive tracking loss based on the state-of-the-art visual tracker, which encourages the model to focus on hard negative distractors during online learning. For data association, we propose Dual Matching Attention Networks (DMAN) with both spatial and temporal attention …
Open set domain recognition has got the attention in recent years. The task aims to specifically classify each sample in the practical unlabeled target domain, which consists of all known classes in the manually labeled source domain and target-specific unknown categories. The absence of annotated training data or auxiliary attribute information for unknown categories makes this task especially difficult. Moreover, exiting domain discrepancy in label space and data distribution further distracts the knowledge transferred from known classes to unknown classes. To address these issues, this work…
Tianjin University（天津大学）；University of Warwick（华威大学）
This paper studies the task of matching image and sentence, where learning appropriate representations across the multi-modal data appears to be the main challenge. Unlike previous approaches that predominantly deploy symmetrical architecture to represent both modalities, we propose Saliency-guided Attention Network (SAN) that asymmetrically employs visual and textual attention modules to learn the fine-grained correlation intertwined between vision and language. The proposed SAN mainly includes three components: saliency detector, Saliency-weighted Visual Attention (SVA) module, and Saliency-…
Shortest-Path Flow Matching with Mixture-Conditioned Bases for OOD Generalization to Unseen Conditions
Wellcome Sanger Institute（Wellcome桑格研究所）；Institute for Molecular Medicine Finland (FIMM)（芬兰分子医学研究所）；University of Helsinki（赫尔辛基大学）；School of Computing and Information Systems（计算与信息系统学院）；University of Cambridge（剑桥大学）；Cambridge Centre for AI in Medicine（剑桥人工智能医学中心）；MediaTek Research（MediaTek研究）；Cambridge Stem Cell Institute（剑桥干细胞研究所）
A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net). D-Net extracts semantic lines by exploiting rich contextual information. To this end, we design the mirror attention module. Then, through pairwise comparisons of extracted semantic lines, we iteratively select the most semantic line and remove redundant ones overlapping with the selected one. For the pairwise comparisons, we develop R-Net and M-Net in the Siamese architecture. Experimen…
