Extracting useful visual cues for the downstream tasks is especially challenging under low-light vision. Prior works create enhanced representations by either correlating visual quality with machine perception or designing illumination-degrading transformation methods that require pre-training on synthetic datasets. We argue that optimizing enhanced image representation pertaining to the loss of the downstream task can result in more expressive representations. Therefore, in this work, we propose a novel module, FeatEnHancer, that hierarchically combines multiscale features using multiheaded attention guided by task-related loss function to create suitable representations. Furthermore, our intra-scale enhancement improves the quality of features extracted at each scale or level, as well as combines features from different scales in a way that reflects their relative importance for the task at hand. FeatEnHancer is a general-purpose plug-and-play module and can be incorporated into any low-light vision pipeline. We show with extensive experimentation that the enhanced representation produced with FeatEnHancer significantly and consistently improves results in several low-light vision tasks, including dark object detection (+5.7 mAP on ExDark), face detection (+1.5 mAPon DARK FACE), nighttime semantic segmentation (+5.1 mIoU on ACDC ), and video object detection (+1.8 mAP on DarkVision), highlighting the effectiveness of enhancing hierarchical features under low-light vision.
Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection
Department of Physics, J.K. Institute of Science（J.K.科学研究所物理系）；World Scientific University（世界科学出版社大学）；University of Intelligent Studies（智能研究大学）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）
The performance of neural network models is often limited by the availability of big data sets. To treat this problem, we survey and develop novel synthetic data generation and augmentation techniques for enhancing low/zero-sample learning in satellite imagery. In addition to extending synthetic data generation approaches, we propose a hierarchical detection approach to improve the utility of synthetic training samples. We consider existing techniques for producing synthetic imagery--3D models and neural style transfer--as well as introducing our own adversarially trained reskinning network, t…
Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze…
HyperVLP: Enhancing Hierarchical Surgical Video-Language Pre-training in Hyperbolic Space
Zhejiang University（浙江大学）；University of Strasbourg, CNRS, INSERM, ICube, UMR7357（斯特拉斯堡大学，CNRS，INSERM，ICube，UMR7357）；IHU Strasbourg（斯特拉斯堡IHU）；Technical University of Munich（慕尼黑工业大学）
HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking
KTH Royal Institute of Technology, Stockholm, Sweden（皇家理工学院）；College of Computer Science, Nankai University, Tianjin, China（南开大学计算机学院）；IIS, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院信息与系统研究所）；Nanjing University of Aeronautics and Astronautics, Nanjing, China（南京航空航天大学）
HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning
School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training
Xi'an Jiaotong University（西安交通大学）；Zhengzhou University（郑州大学）；University of Science and Technology of China（中国科学技术大学）；Dalian University of Technology（大连理工大学）；Zhejiang Lab（浙江实验室）
Automatic mesh-based shape generation is of great interest across a wide range of disciplines, from industrial design to gaming, computer graphics and various other forms of digital art. While most traditional methods focus on primitive based model generation, advances in deep learning made it possible to learn 3-dimensional geometric shape representations in an end-to-end manner. However, most current deep learning based frameworks focus on the representation and generation of voxel and point-cloud based shapes, making it not directly applicable to design and graphics communities. This study …
Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
The human visual system contains a hierarchical sequence of modules that take part in visual perception at superordinate, basic, and subordinate categorization levels. During the last decades, various computational models have been proposed to mimic the hierarchical feed-forward processing of visual cortex, but many critical characteristics of the visual system, such actual neural processing and learning mechanisms, are ignored. Pursuing the line of biological inspiration, we propose a computational model for object recognition in different categorization levels, in which a spiking neural netw…
HCNQA: Enhancing 3D VQA with Hierarchical Concentration Narrowing Supervision
Southern University of Science and Technology, Shenzhen, China（南方科技大学，深圳，中国）；College of William and Mary, Williamsburg, VA 23185, USA（威廉玛丽学院，威廉斯堡，VA 23185，美国）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室，深圳，中国）
Real-world vision based applications require fine-grained classification for various area of interest like e-commerce, mobile applications, warehouse management, etc. where reducing the severity of mistakes and improving the classification accuracy is of utmost importance. This paper proposes a method to boost fine-grained classification through a hierarchical approach via learnable independent query embeddings. This is achieved through a classification network that uses coarse class predictions to improve the fine class accuracy in a stage-wise sequential manner. We exploit the idea of hierar…
HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Shanghai Qizhi Institute（上海智知研究院）
McGill University（麦吉尔大学）；Centre for Intelligent Machines (CIM)（智能机器中心 (CIM)）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, accurate segmentation requires the inclusion of medical images (e.g., T1 post contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline advers…
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）；Dept. of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳州立大学）；Institute of Software Chinese Academy of Science（中国科学院软件研究所）
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；ICube（ICube实验室）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg（斯特拉斯堡大学医院）
Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article we investigate if enhancing the CNN's encoding of shape information can produce more distinguishable features that improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results on a standard benchmark. To confi…
Hefei University of Technology（合肥工业大学）；Soochow University（苏州大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；South China Normal University（华南师范大学）；University of Science and Technology of China（中国科学技术大学）
Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Networks (DenseNet), have achieved great success for image representation by discovering deep hierarchical information. However, most existing networks simply stacks the convolutional layers and hence failing to fully discover local and global feature information among layers. In this paper, we mainly explore how to enhance the local and global dense feature flow by exploiting hierarchical features fully from all the convolution layers. Technically, we propose an efficient and effective CNN framework, i.e., Fast Dense Resid…
HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Research Center of Artificial Intelligence, Peng Cheng Laboratory（鹏城实验室人工智能研究中心）；The Hong Kong University of Science and Technology（香港科学与技术大学）
