Despite convolutional network-based methods have boosted the performance of single image super-resolution (SISR), the huge computation costs restrict their practical applicability. In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A$^2$F) for SISR. Firstly, to explore the features from the bottom layers, the auxiliary feature from all the previous layers are projected into a common space. Then, to better utilize these projected auxiliary features and filter the redundant information, the channel attention is employed to select the most important common feature based on current layer feature. We incorporate these two modules into a block and implement it with a lightweight network. Experimental results on large-scale dataset demonstrate the effectiveness of the proposed model against the state-of-the-art (SOTA) SR methods. Notably, when parameters are less than 320k, A$^2$F outperforms SOTA methods for all scales, which proves its ability to better utilize the auxiliary features. Codes are available at https://github.com/wxxxxxxh/A2F-SR.
University of Arkansas（阿肯色大学）；Concordia University（康考迪亚大学）
Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods have reached some limitations with (1) the requirement of large-scale face recognition (FR) training data with age labels, which is limited in practice; (2) heavy deep network architectures for high performance; and (3) their evaluations are usually taken place on age-related face databases while neglecting the standard large-scale FR databases to guarantee robustness. This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale…
Tsinghua University（清华大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Shanghai Jiao Tong University（上海交通大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）；Peng Cheng Laboratory（鹏城实验室）；ETH Zürich（苏黎世联邦理工学院）
Lightweight image super-resolution (SR) networks have the utmost significance for real-world applications. There are several deep learning based SR methods with remarkable performance, but their memory and computational cost are hindrances in practical usage. To tackle this problem, we propose a Multi-Attentive Feature Fusion Super-Resolution Network (MAFFSRN). MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block. Each FFG contains a stack of proposed multi-attention blocks (MAB) that are combined in a novel feature fusion structure. Further, the M…
Image restoration is the task of recovering a clean image from a degraded version. In most cases, the degradation is spatially varying, and it requires the restoration network to both localize and restore the affected regions. In this paper, we present a new approach suitable for handling the image-specific and spatially-varying nature of degradation in images affected by practically occurring artifacts such as blur, rain-streaks. We decompose the restoration task into two stages of degradation localization and degraded region-guided restoration, unlike existing methods which directly learn a …
Technical University of Munich（慕尼黑工业大学）；Siemens AG（西门子公司）
We propose a lightweight retrieval-based pipeline to predict 6DOF camera poses from RGB images. Our pipeline uses a convolutional neural network (CNN) to encode a query image as a feature vector. A nearest neighbor lookup finds the pose-wise nearest database image. A siamese convolutional neural network regresses the relative pose from the nearest neighboring database image to the query image. The relative pose is then applied to the nearest neighboring absolute pose to obtain the query image's final absolute pose prediction. Our model is a distilled version of NN-Net that reduces its paramete…
Attentive multilayer fusion for vision transformers
Hector Fellow Academy（海克荣誉院士协会）；BIFOLD；University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；Helmholtz Munich（亥姆霍兹慕尼黑）；Technical University Munich（慕尼黑技术大学）；MCML（慕尼黑机器学习中心）；Aignostics, Berlin（柏林Aignostics）；RIKEN AIP（理化学研究所AIP）
Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years. However, most of them ignore the benefit of boundary information. This paper introduces a lightweight dual-resolution network, called DRBANet, aiming to refine semantic segmentation results with the aid of boundary information. DRBANet adopts dual parallel architecture, including: high resolution branch (HRB) and low resolution branch (LRB). Specifically, HRB mainly consists of a set of Efficient Inverted Bottleneck Modules (EIBMs), which learn featur…
Rethinking Theoretical Illumination for Efficient Low-Light Image Enhancement
Department of Electronic Engineering, Chung Yuan Christian University（Chung Yuan Christian University电子工程系）；Department of Electrical Engineering, University of Wisconsin–Milwaukee（University of Wisconsin–Milwaukee电气工程系）；Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University（National Yang Ming Chiao Tung University计算机科学与工程研究所）
vGamba: Attentive State Space Bottleneck for efficient Long-range Dependencies in Visual Recognition
NewraLab；Beihang University（北京航空航天大学）；Beijing GoerTek Alpha Labs（北京歌尔泰克阿尔法实验室）；Imperial College London（伦敦帝国学院）；University of Electronic Science and Technology of China（电子科技大学）
Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate "where" and "what" processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos. The first layer of attention discards the majority of background by selecting a region containing the object of interest, while the subsequent layers tune in on visual features partic…
School of Aerospace Engineering, Tsinghua University（清华大学航天航空学院）
Cross-modality fusing complementary information of multispectral remote sensing image pairs can improve the perception ability of detection algorithms, making them more robust and reliable for a wider range of applications, such as nighttime detection. Compared with prior methods, we think different features should be processed specifically, the modality-specific features should be retained and enhanced, while the modality-shared features should be cherry-picked from the RGB and thermal IR modalities. Following this idea, a novel and lightweight multispectral feature fusion approach with joint…
Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality medical data with a notable domain shift. Given that specific annotated imaging modalities may not be accessible nor complete. Our proposed solution is based on the cross-modality synthesis of medical images to reduce the costly annotation burden by radiologists and bridge the domain gap in radiological images. We present a novel approach for …
University of Padova（帕多瓦大学）；University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and likely destination areas. In this context, multi-modality is a fundamental aspect and its effective modeling can be beneficial to any architecture. Inferring accurate trajectories is nevertheless challenging, due to the inherently uncertain nature of the future. To overcome these difficulties, recent models use different inputs and propose to model human intentions usin…
Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object proposal is a key ingredient in modern object detectors. However, the quality of proposals generated for few-shot classes using existing methods is far worse than that of many-shot classes, e.g., missing boxes for few-shot classes due to misclassification or inaccurate spatial locations with respect to true objects. To address the noisy proposal problem, we propose a novel meta-learning based FSOD model by jointly o…
Lightweight Multiplane Images Network for Real-Time Stereoscopic Conversion from Planar Video
Hefei University of Technology（合肥工业大学）；Peng Cheng National Laboratory（鹏城国家实验室）；Anhui University（安徽大学）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）
University of Southern California（南加州大学）；Duke University（杜克大学）；Syracuse University（雪城大学）
3D object detection from LiDAR data for autonomous driving has been making remarkable strides in recent years. Among the state-of-the-art methodologies, encoding point clouds into a bird's eye view (BEV) has been demonstrated to be both effective and efficient. Different from perspective views, BEV preserves rich spatial and distance information between objects. Yet, while farther objects of the same type do not appear smaller in the BEV, they contain sparser point cloud features. This fact weakens BEV feature extraction using shared-weight convolutional neural networks (CNNs). In order to add…
Hong Kong Baptist University（香港浸会大学）；AWS AI Labs（亚马逊云科技人工智能实验室）；Johns Hopkins University（约翰斯·霍普金斯大学）
Open-set recognition and adversarial defense study two key aspects of deep learning that are vital for real-world deployment. The objective of open-set recognition is to identify samples from open-set classes during testing, while adversarial defense aims to robustify the network against images perturbed by imperceptible adversarial noise. This paper demonstrates that open-set recognition systems are vulnerable to adversarial samples. Furthermore, this paper shows that adversarial defense mechanisms trained on known classes are unable to generalize well to open-set samples. Motivated by these …
AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection
School of Electronics and Information（电子信息学院）；Northwestern Polytechnical University（西北工业大学）；Shaanxi Key Laboratory of Information Acquisition and Processing（陕西省信息获取与处理重点实验室）；Luoyang Institute of Electro-Optical Equipment under AVIC（航空工业集团洛阳光电设备研究所）；VCIP, College of Computer Science（VCIP，计算机科学学院）；Nankai University（南开大学）
T3DNet: Compressing Point Cloud Models for Lightweight 3D Recognition
School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
Boosting Light-Weight Depth Estimation Via Knowledge Distillation
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；South China Normal University（华南师范大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Zhejiang University（浙江大学）；Tohoku University（东北大学（日本））
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization
University of Edinburgh（爱丁堡大学）；Université de Montréal（蒙特利尔大学）；Google Research（谷歌研究院）；Mila（米拉计算科学研究所）；McGill University（麦吉尔大学）；University of Calgary（卡尔加里大学）；CIFAR（加拿大高级研究所）
University of Oxford（牛津大学）；Sun Yat-sen University（中山大学）
Motion deblurring has witnessed rapid development in recent years, and most of the recent methods address it by using deep learning techniques, with the help of different kinds of prior knowledge. Concerning that deblurring is essentially expected to improve the image sharpness, edge information can serve as an important prior. However, the edge has not yet been seriously taken into consideration in previous methods when designing deep models. To this end, we present a novel framework that incorporates edge prior knowledge into deep models, termed Edge Prior Augmented Networks (EPAN). EPAN has…
A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电气与电子工程系）
Malaviya National Institute of Technology Jaipur（斋浦尔马拉维亚国家理工学院）；olx people（OLX人事）
The HGR is a quite challenging task as its performance is influenced by various aspects such as illumination variations, cluttered backgrounds, spontaneous capture, etc. The conventional CNN networks for HGR are following two stage pipeline to deal with the various challenges: complex signs, illumination variations, complex and cluttered backgrounds. The existing approaches needs expert expertise as well as auxiliary computation at stage 1 to remove the complexities from the input images. Therefore, in this paper, we proposes an novel end-to-end compact CNN framework: fine grained feature atte…
CFIS-YOLO: A Lightweight Multi-Scale Fusion Network for Edge-Deployable Wood Defect Detection
School of Information and Engineering, Minzu University of China（中央民族大学信息工程学院）；School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与通信工程学院）；Faculty of Science, The University of Hong Kong（香港大学理学院）
AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification
College of Automation Engineering, Nanhang University（南京航空航天大学自动化工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
