Zobrazeno 1 - 10
of 701
pro vyhledávání: '"Xu, Linfeng"'
Autor:
Xu, Linfeng, Meng, Fanman, Wu, Qingbo, Pan, Lili, Qiu, Heqian, Wang, Lanxiao, Chen, Kailong, Geng, Kanglei, Qian, Yilei, Wang, Haojie, Zhou, Shuchang, Ling, Shimou, Liu, Zejia, Chen, Nanlin, Xu, Yingjie, Cheng, Shaoxu, Tan, Bowen, Xu, Ziyong, Li, Hongliang
The application of activity recognition in the ``AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in manually captured videos and a limited number of activity types, with lit
Externí odkaz:
http://arxiv.org/abs/2410.12337
Recent advancements in prompt tuning have successfully adapted large-scale models like Contrastive Language-Image Pre-trained (CLIP) for downstream tasks such as scene text detection. Typically, text prompt complements the text encoder's input, focus
Externí odkaz:
http://arxiv.org/abs/2409.13576
All the existing entropy stable (ES) schemes for relativistic hydrodynamics (RHD) in the literature were restricted to the ideal equation of state (EOS), which however is often a poor approximation for most relativistic flows due to its inconsistency
Externí odkaz:
http://arxiv.org/abs/2409.10872
Autor:
Qian, Yilei, Geng, Kanglei, Chen, Kailong, Cheng, Shaoxu, Xu, Linfeng, Li, Hongliang, Meng, Fanman, Wu, Qingbo
The application of activity recognition in the "AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in manually captured videos and a limited number of activity types, with litt
Externí odkaz:
http://arxiv.org/abs/2409.03354
Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding Confusion
Autor:
Cheng, Shaoxu, Geng, Kanglei, He, Chiyuan, Qiu, Zihuan, Xu, Linfeng, Qiu, Heqian, Wang, Lanxiao, Wu, Qingbo, Meng, Fanman, Li, Hongliang
Continual Learning (CL) aims to enable Deep Neural Networks (DNNs) to learn new data without forgetting previously learned knowledge. The key to achieving this goal is to avoid confusion at the feature level, i.e., avoiding confusion within old tasks
Externí odkaz:
http://arxiv.org/abs/2408.02695
Autor:
Chen, Shuai, Meng, Fanman, Wu, Chenhao, Wei, Haoran, Zhang, Runtong, Wu, Qingbo, Xu, Linfeng, Li, Hongliang
Few-Shot Segmentation (FSS) aims to segment novel classes using only a few annotated images. Despite considerable process under pixel-wise support annotation, current FSS methods still face three issues: the inflexibility of backbone upgrade without
Externí odkaz:
http://arxiv.org/abs/2407.16182
Audio-Visual Segmentation (AVS) aims to extract the sounding object from a video frame, which is represented by a pixel-wise segmentation mask for application scenarios such as multi-modal video editing, augmented reality, and intelligent robot syste
Externí odkaz:
http://arxiv.org/abs/2310.06259
Publikováno v:
Published by IEEE Transactions on Signal and Information Processing over Networks,2022
This study proposes a coupled velocity model (CVM) that establishes the relation between the orientation and velocity using their correlation, avoiding that the existing extended object tracking (EOT) models treat them as two independent quantities.
Externí odkaz:
http://arxiv.org/abs/2308.12723
Autor:
Xu, Linfeng, Wu, Qingbo, Pan, Lili, Meng, Fanman, Li, Hongliang, He, Chiyuan, Wang, Hanxin, Cheng, Shaoxu, Dai, Yu
Publikováno v:
in IEEE Transactions on Multimedia, vol. 26, pp. 2430-2443, 2024
With the rapid development of wearable cameras, a massive collection of egocentric video for first-person visual perception becomes available. Using egocentric videos to predict first-person activity faces many challenges, including limited field of
Externí odkaz:
http://arxiv.org/abs/2301.10931
Recent years have witnessed the great success of blind image quality assessment (BIQA) in various task-specific scenarios, which present invariable distortion types and evaluation criteria. However, due to the rigid structure and learning framework,
Externí odkaz:
http://arxiv.org/abs/2209.07126