no code implementations • 25 Jan 2024 • Zeyu Xi, Ge Shi, Xuefen Li, Junchi Yan, Zun Li, Lifang Wu, Zilin Liu, Liang Wang
We develop a knowledge guided entity-aware video captioning network (KEANet) based on a candidate player list in encoder-decoder form for basketball live text broadcast.
no code implementations • 28 Feb 2023 • Xianglong Lang, Zhuming Wang, Zun Li, Meng Tian, Ge Shi, Lifang Wu, Liang Wang
Specifically, the framework consists of a Visual Representation Module to extract individual appearance features, a Knowledge Augmented Semantic Relation Module explore semantic representations of individual actions, and a Knowledge-Semantic-Visual Interaction Module aims to integrate visual and semantic information by the knowledge.
no code implementations • 15 Aug 2022 • Chaoyun Zhang, Kai Wang, Hao Chen, Ge Fan, Yingjie Li, Lifang Wu, Bingchao Zheng
However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player.
no code implementations • 26 Jan 2022 • Sinuo Deng, Lifang Wu, Ge Shi, Lehao Xing, Meng Jian, Ye Xiang
We first introduce a prompt tuning method that mimics the pretraining objective of CLIP and thus can leverage the rich image and text semantics entailed in CLIP.
no code implementations • 13 Jul 2020 • Lifang Wu, Zhou Yang, Qi. Wang, Meng Jian, Boxuan Zhao, Junchi Yan, Chang Wen Chen
Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos.
no code implementations • IEEE Access 2020 • Dezhong Xu, HENG FU, Lifang Wu, Meng Jian, Dong Wang, AND XU LIU
Then, we propose two types of inference models, opt-GRU and relation-GRU, which are used to encode the object relationship and motion representation effectively, and form the discriminative frame-level feature representation.
Ranked #4 on Group Activity Recognition on Volleyball
15 code implementations • 24 Apr 2019 • Yonghao He, Dezhong Xu, Lifang Wu, Meng Jian, Shiming Xiang, Chunhong Pan
Under the new schema, the proposed method can achieve superior accuracy (WIDER FACE Val/Test -- Easy: 0. 910/0. 896, Medium: 0. 881/0. 865, Hard: 0. 780/0. 770; FDDB -- discontinuous: 0. 973, continuous: 0. 724).
Ranked #6 on Face Detection on FDDB
no code implementations • 16 Mar 2019 • Lifang Wu, Zhou Yang, Jiaoyu He, Meng Jian, Yaowen Xu, Dezhong Xu, Chang Wen Chen
Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion.