In this paper, we examine a key limitation in query-based detectors for temporal action detection (TAD), which arises from their direct adaptation of originally designed architectures for object detection. Despite the effectiveness of the existing models, they struggle to fully address the unique challenges of TAD, such as the redundancy in multi-scale features and the limited ability to capture sufficient temporal context. To address these issues, we propose a multi-dilated gated encoder and central-adjacent region integrated decoder for temporal action detection transformer (DiGIT). Our approach replaces the existing encoder that consists of multi-scale deformable attention and feedforward network with our multi-dilated gated encoder. Our proposed encoder reduces the redundant information caused by multi-level features while maintaining the ability to capture fine-grained and long-range temporal information. Furthermore, we introduce a central-adjacent region integrated decoder that leverages a more comprehensive sampling strategy for deformable cross-attention to capture the essential information. Extensive experiments demonstrate that DiGIT achieves state-of-the-art performance on THUMOS14, ActivityNet v1.3, and HACS-Segment. Code is available at: https://github.com/Dotori-HJ/DiGIT
CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；School of Computer Science, the University of Sydney（悉尼大学计算机学院）；China United Network Communications Group Co., Ltd.（中国联合网络通信集团有限公司）
Monash University Malaysia（莫纳什大学马来西亚分校）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；CNRS（法国国家科学研究中心）；Singapore Eye Research Institute（新加坡眼科研究所）；University of Oklahoma（俄克拉荷马大学）
Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size of a nodule is very diverse and can range anywhere between 3 and 30 millimeters. The high variation of nodule sizes makes classifying them a difficult and challenging task. In this study, we propose a novel CNN architecture called Gated-Dilated (GD) networks to classify nodules as malignant or benign. Unlike previous studies, the GD network uses multiple dilated convolutions instead of max-poolings to capture the scale variations. Mor…
Recently, some pioneering works have preferred applying more complex modules to improve segmentation performances. However, it is not friendly for actual clinical environments due to limited computing resources. To address this challenge, we propose a light-weight model to achieve competitive performances for skin lesion segmentation at the lowest cost of parameters and computational complexity so far. Briefly, we propose four modules: (1) DGA consists of dilated convolution and gated attention mechanisms to extract global and local feature information; (2) IEA, which is based on external atte…
Multi-Scale Fruit Capsules: Dilated Convolutions and Dynamic Routing for In-the-Wild Explainable Fruit Recognition
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；Indian Institute of Science Education and Research Bhopal（印度科学教育与研究学院博帕尔分校）；UJM-Saint-Etienne（圣埃蒂安大学）；CNRS（法国国家科学研究中心）；Laboratoire Hubert Curien（于贝尔·居里安实验室）
School of Computer Science, Fudan University（复旦大学计算机学院）；Systems and Shanghai Key Laboratory of Data Science, Fudan University（复旦大学系统与上海数据科学重点实验室）；Shanghai Insitute of Intelligent Electroics & Systems（上海智能电子与系统研究所）；Bytedance AI Lab（字节跳动AI实验室）；Singapore Management University（新加坡管理大学）
Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless…
Carnegie Mellon University（卡内基梅隆大学）；Delft University of Technology（代尔夫特理工大学）
Typical techniques for sequence classification are designed for well-segmented sequences which have been edited to remove noisy or irrelevant parts. Therefore, such methods cannot be easily applied on noisy sequences expected in real-world applications. In this paper, we present the Temporal Attention-Gated Model (TAGM) which integrates ideas from attention models and gated recurrent networks to better deal with noisy or unsegmented sequences. Specifically, we extend the concept of attention model to measure the relevance of each observation (time step) of a sequence. We then use a novel gated…
R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detection
SAFE Center, Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU)（安全中心，信息安全与通信技术系，挪威科学技术大学（NTNU））
Currently, developments of deep learning techniques are providing instrumental to identify, classify, and quantify patterns in medical images. Segmentation is one of the important applications in medical image analysis. In this regard, U-Net is the predominant approach to medical image segmentation tasks. However, we found that those U-Net based models have limitations in several aspects, for example, millions of parameters in the U-Net consuming considerable computation resource and memory, lack of global information, and missing some tough objects. Therefore, we applied two modifications to …
CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learning
Qianan College, North China University of Science and Technology（华北科技大学千安学院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络安全学院）；College of Science, North China University of Science and Technology（华北科技大学理学院）
