3 citations · 5 across the 13 of their papers we have counts for
15 papers
P2P-Former: Bridging the Supervision Gap Between Point and Proposal in Temporal Action Localization
Yunchuan Ma, Laiyun Qing, Sai Xu +2
Recently, point-supervised temporal action localization has attracted increasing attention due to its low annotation cost (only one frame per action instance) and competitive detec…
UltraDub: Towards Authentic Dubbing by Unifying Visually-Steered Flow Learning and Trajectory Guidance
Gaoxiang Cong, Liang Li, Jianwei Wen +3
Visual voice cloning requires intelligible, speaker-consistent speech synchronized with visible articulation. However, sequential multimodal conditioning can disrupt previously est…
CoSyncDiT: Cognitive Synchronous Diffusion Transformer for Movie Dubbing
Gaoxiang Cong, Liang Li, Jiaxin Ye +4
Movie dubbing aims to synthesize speech that preserves the vocal identity of a reference audio while synchronizing with the lip movements in a target video. Existing methods fail t…
Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization
Yunchuan Ma, Laiyun Qing, Guorong Li +3
Point-supervised Temporal Action Localization (PTAL) adopts a lightly frame-annotated paradigm (\textit{i.e.}, labeling only a single frame per action instance) to train a model to…
Boosting Point-supervised Temporal Action Localization via Text Refinement and Alignment
Yunchuan Ma, Laiyun Qing, Guorong Li +3
Recently, point-supervised temporal action localization has gained significant attention for its effective balance between labeling costs and localization accuracy. However, curren…
FlowDubber: Movie Dubbing with LLM-based Semantic-aware Learning and Flow Matching based Voice Enhancing
Gaoxiang Cong, Liang Li, Jiadong Pan +5
Movie Dubbing aims to convert scripts into speeches that align with the given movie clip in both temporal and emotional aspects while preserving the vocal timbre of a given brief r…
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…
The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning
Mingkai Tian, Guorong Li, Yuankai Qi +4
Zero-shot video captioning requires that a model generate high-quality captions without human-annotated video-text pairs for training. State-of-the-art approaches to the problem le…
Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization
Zhuo Tao, Liang Li, Qi Chen +5
Natural language video localization (NLVL) is a crucial task in video understanding that aims to localize the target moment in videos specified by a given language description. Rec…
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…
RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning
Yunchuan Ma, Laiyun Qing, Guorong Li +4
Despite the significant progress of fully-supervised video captioning, zero-shot methods remain much less explored. In this paper, we propose a novel zero-shot video captioning fra…
StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing
Gaoxiang Cong, Yuankai Qi, Liang Li +6
Given a script, the challenge in Movie Dubbing (Visual Voice Cloning, V2C) is to generate speech that aligns well with the video in both time and emotion, based on the tone of a re…
Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting
Xinyan Liu, Guorong Li, Yuankai Qi +5
Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. % Existing methods learn representations based on trajectory labels for individu…
Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection
Chen Zhang, Guorong Li, Yuankai Qi +4
The goal of weakly supervised video anomaly detection is to learn a detection model using only video-level labeled data. However, prior studies typically divide videos into fixed-l…
Towards Subject-Oriented Video Captioning via User-Specified Targets
Chang Teng, Yunchuan Ma, Guorong Li +3
Describing video content according to users' needs is a long-held goal. Although existing video captioning methods have made significant progress, the generated captions may not fo…