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Liuzhuozheng Li

5 accepted papers

2026

Deforming Videos to Masks: Flow Matching for Referring Video Segmentation

ICLR 2026poster

Referring Video Object Segmentation (RVOS) requires segmenting specific objects in a video guided by a natural language description. The core challenge of RVOS is to anchor abstract linguistic concepts onto a specific set of pixels and continuously segment them through the complex dynamics of a vide…

Cited by 0SourceScholar
2026

RefTon: Reference person shot assist virtual Try-on

CVPR 2026

We introduce RefTon, a flux-based person-to-person virtual try-on framework that enhances garment realism through unpaired visual references. Unlike conventional approaches that rely on complex auxiliary inputs such as body parsing and warped mask or require finely designed extract branches to proce

Cited by 0SourcecodeScholar
2026

Representation Alignment for Diffusion Transformers without External Components

ICLR 2026poster

Recent studies have demonstrated that learning a meaningful internal represen- tation can accelerate generative training. However, existing approaches necessi- tate to either introduce an off-the-shelf external representation task or rely on a large-scale, pre-trained external representation encoder…

Cited by 0SourcecodeScholar
2026

SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training

CVPR 2026

Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as REPA (relying on external representation encoders) or SRA (requiring dual-model setups), inevitably incur heavy computati

Cited by 0SourceScholar
2024

Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification

EMNLP 2024main

Recent advances in fine-tuning Vision-Language Models (VLMs) have witnessed the success of prompt tuning and adapter tuning, while the classic model fine-tuning on inherent parameters seems to be overlooked. It is believed that fine-tuning the parameters of VLMs with few-shot samples corrupts the pr…