Search Results for author: Jiahao Zhu

Found 5 papers, 0 papers with code

EFLNet: Enhancing Feature Learning for Infrared Small Target Detection

no code implementations27 Jul 2023 Bo Yang, Xinyu Zhang, Jiahao Zhu, Jian Zhang, Dongjian Tian, Jun Luo, Mingliang Zhou, Yangjun Pi

Single-frame infrared small target detection is considered to be a challenging task, due to the extreme imbalance between target and background, bounding box regression is extremely sensitive to infrared small targets, and small target information is easy to lose in the high-level semantic layer.

regression

Tracking Objects and Activities with Attention for Temporal Sentence Grounding

no code implementations21 Feb 2023 Zeyu Xiong, Daizong Liu, Pan Zhou, Jiahao Zhu

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects.

Sentence Temporal Sentence Grounding

Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding

no code implementations2 Jan 2023 Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong

All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning.

Sentence Temporal Sentence Grounding

3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds

no code implementations18 May 2022 Jiahao Zhu, Huajun Zhou, Zixuan Chen, Yi Zhou, Xiaohua Xie

3D deep models consuming point clouds have achieved sound application effects in computer vision.

Adversarial Attack Steganalysis

Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care

no code implementations19 May 2021 Hua Zheng, Jiahao Zhu, Wei Xie, Judy Zhong

We developed a machine learning algorithm, based on a deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critical ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.

Management reinforcement-learning +1

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