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With the rapid development of deep learning algorithms, action recognition in video has achieved many important research results. One issue in action recognition, Zero-Shot Action Recognition (ZSAR), has recently attracted considerable…

计算机视觉与模式识别 · 计算机科学 2020-04-24 Dong Cao , Lisha Xu , HaiBo Chen

Action recognition is a fundamental capability for humanoid robots to interact and cooperate with humans. This application requires the action recognition system to be designed so that new actions can be easily added, while unknown actions…

机器人学 · 计算机科学 2025-09-16 Stefano Berti , Andrea Rosasco , Michele Colledanchise , Lorenzo Natale

Pre-trained large-scale vision-language models (VLMs) have acquired profound understanding of general visual concepts. Recent advancements in efficient transfer learning (ETL) have shown remarkable success in fine-tuning VLMs within the…

计算机视觉与模式识别 · 计算机科学 2024-08-21 Haoxing Chen , Yaohui Li , Zizheng Huang , Yan Hong , Zhuoer Xu , Zhangxuan Gu , Jun Lan , Huijia Zhu , Weiqiang Wang

Cardiopulmonary resuscitation (CPR) is alongside electrical defibrillation the most crucial countermeasure for sudden cardiac arrest, which affects thousands of individuals every year. In this paper, we present a novel approach including…

神经与进化计算 · 计算机科学 2020-10-26 Christian Lins , Daniel Eckhoff , Andreas Klausen , Sandra Hellmers , Andreas Hein , Sebastian Fudickar

Despite the rapid progress, existing works on action understanding focus strictly on one type of action agent, which we call actor---a human adult, ignoring the diversity of actions performed by other actors. To overcome this narrow…

计算机视觉与模式识别 · 计算机科学 2017-05-01 Chenliang Xu , Caiming Xiong , Jason J. Corso

Compound Expression Recognition (CER) plays a crucial role in interpersonal interactions. Due to the existence of Compound Expressions , human emotional expressions are complex, requiring consideration of both local and global facial…

计算机视觉与模式识别 · 计算机科学 2024-03-20 Jun Yu , Jichao Zhu , Wangyuan Zhu

Home-based physical therapies are effective if the prescribed exercises are correctly executed and patients adhere to these routines. This is specially important for older adults who can easily forget the guidelines from therapists.…

Cardiac Magnetic Resonance Imaging (MRI) plays an important role in the analysis of cardiac function. However, the acquisition is often accompanied by motion artefacts because of the difficulty of breath-hold, especially for acute symptoms…

图像与视频处理 · 电气工程与系统科学 2022-10-17 Ruizhe Li , Xin Chen

Echocardiogram video plays a crucial role in analysing cardiac function and diagnosing cardiac diseases. Current deep neural network methods primarily aim to enhance diagnosis accuracy by incorporating prior knowledge, such as segmenting…

图像与视频处理 · 电气工程与系统科学 2024-10-29 Jiewen Yang , Yiqun Lin , Bin Pu , Jiarong Guo , Xiaowei Xu , Xiaomeng Li

This paper presents a novel approach for automatic recognition of human activities for video surveillance applications. We propose to represent an activity by a combination of category components, and demonstrate that this approach offers…

计算机视觉与模式识别 · 计算机科学 2015-03-03 Weiyao Lin , Ming-Ting Sun , Radha Poovendran , Zhengyou Zhang

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the `nnU-Net'…

图像与视频处理 · 电气工程与系统科学 2021-07-23 Esther Puyol-Antón , Baldeep S. Sidhu , Justin Gould , Bradley Porter , Mark K. Elliott , Vishal Mehta , Christopher A. Rinaldi , Andrew P. King

Deep learning models have achieved state-of-the- art performance in recognizing human activities, but often rely on utilizing background cues present in typical computer vision datasets that predominantly have a stationary camera. If these…

机器人学 · 计算机科学 2017-09-20 Fahimeh Rezazadegan , Sareh Shirazi , Ben Upcroft , Michael Milford

Human action recognition plays a critical role in healthcare and medicine, supporting applications such as patient behavior monitoring, fall detection, surgical robot supervision, and procedural skill assessment. While traditional models…

计算机视觉与模式识别 · 计算机科学 2025-08-01 Utkarsh Shandilya , Marsha Mariya Kappan , Sanyam Jain , Vijeta Sharma

Understanding driver activity is vital for in-vehicle systems that aim to reduce the incidence of car accidents rooted in cognitive distraction. Automating real-time behavior recognition while ensuring actions classification with high…

计算机视觉与模式识别 · 计算机科学 2021-02-23 Chaoyun Zhang , Rui Li , Woojin Kim , Daesub Yoon , Paul Patras

Model tracing and constraint-based modeling are two approaches to diagnose student input in stepwise tasks. Model tracing supports identifying consecutive problem-solving steps taken by a student, whereas constraint-based modeling supports…

人工智能 · 计算机科学 2025-07-21 Gerben van der Hoek , Johan Jeuring , Rogier Bos

Mistake detection in procedural tasks is essential for building intelligent systems that support learning and task execution. Existing approaches primarily analyze how an action is performed, while overlooking what it produces, i.e., the…

计算机视觉与模式识别 · 计算机科学 2026-02-17 Wenliang Guo , Yujiang Pu , Yu Kong

Good communication is essential within teams dealing with emergency situations. In this paper we look at communications within a resuscitation team performing cardio-pulmonary resuscitation. Communication underpins efficient collaboration,…

计算机与社会 · 计算机科学 2019-04-09 Lyuba Mancheva , Julie Dugdale

Classifying the behavior of humans or animals from videos is important in biomedical fields for understanding brain function and response to stimuli. Action recognition, classifying activities performed by one or more subjects in a trimmed…

计算机视觉与模式识别 · 计算机科学 2023-01-18 Michael Perez , Corey Toler-Franklin

This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must comply with certain constraints. These constraints are crucial…

机器学习 · 计算机科学 2023-06-30 Kazumi Kasaura , Shuwa Miura , Tadashi Kozuno , Ryo Yonetani , Kenta Hoshino , Yohei Hosoe