most citedEmoDubber: Towards High Quality and Emotion Controllable Movie Dubbing

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

SDVPT: Semantic-Driven Visual Prompt Tuning for Open-World Object Counting

Yiming Zhao, Guorong Li, Laiyun Qing +5

Open-world object counting leverages the robust text-image alignment of pre-trained vision-language models (VLMs) to enable counting of arbitrary categories in images specified by…

cs.CV2025

ProgRoCC: A Progressive Approach to Rough Crowd Counting

Shengqin Jiang, Linfei Li, Haokui Zhang +6

As the number of individuals in a crowd grows, enumeration-based techniques become increasingly infeasible and their estimates increasingly unreliable. We propose instead an estima…

cs.CV2025

Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning

Huajie Jiang, Zhengxian Li, Xiaohan Yu +4

Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. It inevitably requires c…

cs.SD2024★ 1 cited

EmoDubber: Towards High Quality and Emotion Controllable Movie Dubbing

Gaoxiang Cong, Jiadong Pan, Liang Li +5

Given a piece of text, a video clip, and a reference audio, the movie dubbing task aims to generate speech that aligns with the video while cloning the desired voice. The existing…

cs.SD2024

Generating High-quality Symbolic Music Using Fine-grained Discriminators

Zhedong Zhang, Liang Li, Jiehua Zhang +5

Existing symbolic music generation methods usually utilize discriminator to improve the quality of generated music via global perception of music. However, considering the complexi…