Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors, facilitating better-performing target detection and autonomous navigation in a wide range of robotics applications. It is noted that the state-of-the-art SR methods are typically trained and tested using single-channel inputs, neglecting the fact that the cost of capturing high-resolution images in different spectral domains varies significantly. In this paper, we attempt to leverage complementary information from a low-cost channel (visible/depth) to boost image quality of an expensive channel (thermal) using fewer parameters. To this end, we first present an effective method to virtually generate pixel-wise aligned visible and thermal images based on real-time 3D reconstruction of multi-modal data captured at various viewpoints. Then, we design a feature-level multispectral fusion residual network model to perform high-accuracy SR of thermal images by adaptively integrating co-occurrence features presented in multispectral images. Experimental results demonstrate that this new approach can effectively alleviate the ill-posed inverse problem of image SR by taking into account complementary information from an additional low-cost channel, significantly outperforming state-of-the-art SR approaches in terms of both accuracy and efficiency.
The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation
Nankai University（南开大学）；South China University of Technology（华南理工大学）；Peking University（北京大学）；Henan Institute of Science and Technology（河南科技学院）；MicroBT Electronics Technology Co., Ltd（深圳比特微电子科技有限公司）
Beijing Institute of Technology（北京理工大学）；Alibaba Group（阿里巴巴集团）；University of Rochester（罗切斯特大学）
Entity-aware image captioning aims to describe named entities and events related to the image by utilizing the background knowledge in the associated article. This task remains challenging as it is difficult to learn the association between named entities and visual cues due to the long-tail distribution of named entities. Furthermore, the complexity of the article brings difficulty in extracting fine-grained relationships between entities to generate informative event descriptions about the image. To tackle these challenges, we propose a novel approach that constructs a multi-modal knowledge …
Boosting Multimodal Learning via Disentangled Gradient Learning
The Laboratory of Intelligent Collaborative Computing of UESTC（UESTC智能协同计算实验室）；The School of Information and Communication Engineering of UESTC（UESTC信息与通信工程学院）
Daimler AG（戴姆勒股份公司）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Forschungszentrum Informatik (FZI)（信息技术研究中心）
Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central component of state-of-the-art perception systems. In addition to accurate spatial perception, a comprehensive semantic understanding of the environment is essential for efficient and safe operation. In this paper we present a novel deep neural network architecture called LiLaNet for point-wise, multi-class semantic labeling of semi-dense LiDAR data. The network utilizes virtual image projections of the 3D point clouds for e…
SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts
Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University（厦门大学福建省智慧城市感知与计算重点实验室）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（厦门大学多媒体可信感知与高效计算教育部重点实验室）
Boosting Multi-modal Model Performance with Adaptive Gradient Modulation
ShanghaiTech University（上海科技大学）；Shanghai Innovation Center for Processor Technologies（上海处理器技术创新中心）；Shanghai Center for Brain Science and Brain-Inspired Technology（上海脑科学与类脑研究中心）；University of Science and Technology of China（中国科学技术大学）
Baidu Inc.（百度公司）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Tianjin University（天津大学）；Aberystwyth University（阿伯里斯特威斯大学）
Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video). Existing methods typically employ completely heterogeneous visual-textual information to align video and text, whilst lacking the awareness of homogeneous high-level semantic information residing in both modalities. To fill this gap, in this work, we propose a novel visual-linguistic aligning model named HiSE for VTR, which improves the cross-modal representation by incorporating explicit high-level semantics. First, we explore th…
ReconBoost: Boosting Can Achieve Modality Reconcilement
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences（中国科学院大数据挖掘与知识管理重点实验室）
Gwangju Institute of Science and Technology（光州科学技术院）
Autonomous vehicles are conceived to provide safe and secure services by validating the safety standards as indicated by SOTIF-ISO/PAS-21448 (Safety of the intended functionality). Keeping in this context, the perception of the environment plays an instrumental role in conjunction with localization, planning and control modules. As a pivotal algorithm in the perception stack, object detection provides extensive insights into the autonomous vehicle's surroundings. Camera and Lidar are extensively utilized for object detection among different sensor modalities, but these exteroceptive sensors ha…
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Monash University（莫纳什大学）；Temple University（天普大学）；Peng Cheng Laboratory（鹏城实验室）
FauForensics: Boosting Audio-Visual Deepfake Detection with Facial Action Units
School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机科学学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））
Improving multi-view aggregation is integral for multi-view pedestrian detection, which aims to obtain a bird's-eye-view pedestrian occupancy map from images captured through a set of calibrated cameras. Inspired by the success of attention modules for deep neural networks, we first propose a Homography Attention Module (HAM) which is shown to boost the performance of existing end-to-end multiview detection approaches by utilizing a novel channel gate and spatial gate. Additionally, we propose Booster-SHOT, an end-to-end convolutional approach to multiview pedestrian detection incorporating ou…
Boosting Single-domain Generalized Object Detection via Vision-Language Knowledge Interaction
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（先进交叉学科研究院，中国科学院大学）；Institute of Microelectronics of the Chinese Academy of Sciences（中国科学院微电子研究所）
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation
Samsung R&D Institute China–Beijing（三星中国北京研发中心）；Beihang University（北京航空航天大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；The University of Sydney（悉尼大学）
Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）
University of Glasgow（格拉斯哥大学）；Optos (Nikon)（Optos（尼康））
We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina. Images captured in both reflectance and autofluorescence (AF) modes, and central pole and eyesteered gazes, were used. The method achieved an OD localisation accuracy of 99.4% within one OD radius, and fovea localisation accuracy of 99.1% within one OD radius on a test set comprising of 1790 images. The performance of fovea localisation in AF images was comparable to the variation between human annotato…
3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with rich semantic texture information from images at the cost of extra network designs and overhead. In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference. Our key design is to first exploit the potential instructive semantic knowledge within t…
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算先进创新中心）；Beihang University（北京航空航天大学）；Hangzhou International Innovation Institute（杭州国际创新研究院）；Xreal；Renmin University（中国人民大学）；Peking University（北京大学）；Beijing Academy of Blockchain and Edge Computing (BABEC)（北京区块链与边缘计算研究院）
Egocentric activity recognition in first-person videos has an increasing importance with a variety of applications such as lifelogging, summarization, assisted-living and activity tracking. Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature. In this work, we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning (MKL) and multi-kernel boosting (MKBoost). For that purpose, firstly grid optical-flow, virtual-inertia feature, log-cova…
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI
Xiamen University（厦门大学）；Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Fudan University（复旦大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；University of Shanghai for Science and Technology（上海理工大学）；GE Healthcare（通用电气医疗集团）；Siemens Healthineers Ltd.（西门子医疗有限公司）；The Hong Kong Polytechnic University（香港理工大学）；Pontificia Universidad Católica de Chile（智利天主教大学）
3D object detection has seen quick progress thanks to advances in deep learning on point clouds. A few recent works have even shown state-of-the-art performance with just point clouds input (e.g. VoteNet). However, point cloud data have inherent limitations. They are sparse, lack color information and often suffer from sensor noise. Images, on the other hand, have high resolution and rich texture. Thus they can complement the 3D geometry provided by point clouds. Yet how to effectively use image information to assist point cloud based detection is still an open question. In this work, we build…
Classifying the confusing samples in the course of RGBT tracking is a quite challenging problem, which hasn't got satisfied solution. Existing methods only focus on enlarging the boundary between positive and negative samples, however, the structured information of samples might be harmed, e.g., confusing positive samples are closer to the anchor than normal positive samples.To handle this problem, we propose a novel Multi-Modal Multi-Margin Metric Learning framework, named M$^5$L for RGBT tracking in this paper. In particular, we design a multi-margin structured loss to distinguish the confus…
Fine-grained action recognition datasets exhibit environmental bias, where multiple video sequences are captured from a limited number of environments. Training a model in one environment and deploying in another results in a drop in performance due to an unavoidable domain shift. Unsupervised Domain Adaptation (UDA) approaches have frequently utilised adversarial training between the source and target domains. However, these approaches have not explored the multi-modal nature of video within each domain. In this work we exploit the correspondence of modalities as a self-supervised alignment a…
Face recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations. Recently several low-rank dictionary learning methods have been proposed and achieved promising results for noisy observations. While these methods are mostly developed for single-modality scenarios, recent studies demonstrated the advantages of feature fusion from multiple inputs. We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, u…
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；School of Software, Shandong University（山东大学软件学院）；School of Engineering and Sustainable Development, De Montfort University（德蒙福特大学工程与可持续发展学院）
Department of Forest Sciences, University of Helsinki（赫尔辛基大学森林科学系）；School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China（武汉大学计算机学院、国家多媒体软件工程研究中心、人工智能研究院、湖北省多媒体与网络通信工程重点实验室，武汉，中国）
QCFace: Image Quality Control for boosting Face Representation & Recognition
Faculty of Computer Science and Engineering（计算机科学与工程学院）；Advanced Institute of Interdisciplinary Science and Technology（跨学科科学与技术高级研究所）；Ho Chi Minh City University of Technology (HCMUT)（胡志明市技术大学）；School of Clinical Sciences（临床科学学院）；Monash University（莫纳什大学）；Monash Health（莫纳什健康）
We address the problem of visible-infrared person re-identification (VI-reID), that is, retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal setting. Two main challenges in VI-reID are intra-class variations across person images, and cross-modal discrepancies between visible and infrared images. Assuming that the person images are roughly aligned, previous approaches attempt to learn coarse image- or rigid part-level person representations that are discriminative and generalizable across different modalities. However, the person images, typically cropped…
Shanghai Institute for Advanced Communication and Data Science, Shanghai University（上海大学上海先进通信与数据科学研究院）
Face Anti-spoofing (FAS) is a challenging problem due to complex serving scenarios and diverse face presentation attack patterns. Especially when captured images are low-resolution, blurry, and coming from different domains, the performance of FAS will degrade significantly. The existing multi-modal FAS datasets rarely pay attention to the cross-domain problems under deployment scenarios, which is not conducive to the study of model performance. To solve these problems, we explore the fine-grained differences between multi-modal cameras and construct a cross-domain multi-modal FAS dataset unde…
Data-driven approaches to assist operating room (OR) workflow analysis depend on large curated datasets that are time consuming and expensive to collect. On the other hand, we see a recent paradigm shift from supervised learning to self-supervised and/or unsupervised learning approaches that can learn representations from unlabeled datasets. In this paper, we leverage the unlabeled data captured in robotic surgery ORs and propose a novel way to fuse the multi-modal data for a single video frame or image. Instead of producing different augmentations (or 'views') of the same image or video frame…
