1 code implementation • 10 Aug 2023 • Huilin Zhu, Jingling Yuan, Xian Zhong, Zhengwei Yang, Zheng Wang, Shengfeng He
Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets.
1 code implementation • CVPR 2023 • Zhengwei Yang, Meng Lin, Xian Zhong, Yu Wu, Zheng Wang
Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re-IDentification (ReID).
1 code implementation • 28 Nov 2022 • Xian Zhong, Zipeng Li, Shuqin Chen, Kui Jiang, Chen Chen, Mang Ye
In this paper, we introduce a novel Refined Semantic enhancement method towards Frequency Diffusion (RSFD), a captioning model that constantly perceives the linguistic representation of the infrequent tokens.
no code implementations • 16 Oct 2021 • Zhixin Sun, Xian Zhong, Shuqin Chen, Lin Li, Luo Zhong
Video captioning is a challenging task that captures different visual parts and describes them in sentences, for it requires visual and linguistic coherence.
1 code implementation • 21 Jul 2020 • Xian Zhong, Cheng Gu, Wenxin Huang, Lin Li, Shuqin Chen, Chia-Wen Lin
As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks.
Ranked #17 on Few-Shot Image Classification on FC100 5-way (5-shot)
no code implementations • 4 Oct 2018 • Zhongwei Xie, Lin Li, Xian Zhong, Luo Zhong
In this paper, we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra information. Concretely speaking, cross-modal embeddings from image captioning and video captioning models are reused to help learned features be projected into a coordinated space, where similarity can be directly computed.