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20242026
most citedProgressive Depth Decoupling and Modulating for Flexible Depth Completion

1 citations · 2 across the 13 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation

Peihua Deng, Jiehua Zhang, Xichun Sheng +4

This paper explores the Class-Incremental Source-Free Unsupervised Domain Adaptation (CI-SFUDA) problem, where the unlabeled target data come incrementally without access to labele…

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…

cs.CV2024

Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning

Yunbin Tu, Liang Li, Li Su +2

Change captioning aims to succinctly describe the semantic change between a pair of similar images, while being immune to distractors (illumination and viewpoint changes). Under th…

cs.CV2024

Technique Report of CVPR 2024 PBDL Challenges

Ying Fu, Yu Li, Shaodi You +96

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…

cs.CV2024

Context-aware Difference Distilling for Multi-change Captioning

Yunbin Tu, Liang Li, Li Su +3

Multi-change captioning aims to describe complex and coupled changes within an image pair in natural language. Compared with single-change captioning, this task requires the model…

cs.CV2024★ 1 cited

Progressive Depth Decoupling and Modulating for Flexible Depth Completion

Zhiwen Yang, Jiehua Zhang, Liang Li +3

Image-guided depth completion aims at generating a dense depth map from sparse LiDAR data and RGB image. Recent methods have shown promising performance by reformulating it as a cl…

cs.CL2024★ 1 cited

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…