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20242026
most citedMuseBarControl: Enhancing Fine-Grained Control in Symbolic Music Generation through Pre-Training and Counterfactual Loss

2 citations · 2 across the 9 of their papers we have counts for

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cs.CV2026

Training-free Motion Factorization for Compositional Video Generation

Zixuan Wang, Ziqin Zhou, Feng Chen +4

Compositional video generation aims to synthesize multiple instances with diverse appearance and motion. However, current approaches mainly focus on binding semantics, neglecting t…

cs.CV2026

Enhancing Text-to-Image Generation via End-Edge Collaborative Hybrid Super-Resolution

Chongbin Yi, Yuxin Liang, Ziqi Zhou +1

Artificial Intelligence-Generated Content (AIGC) has made significant strides, with high-resolution text-to-image (T2I) generation becoming increasingly critical for improving user…

cs.CV2025

Visual Self-Refinement for Autoregressive Models

Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi +5

Autoregressive models excel in sequential modeling and have proven to be effective for vision-language data. However, the spatial nature of visual signals conflicts with the sequen…

cs.CV2025

TATTOO: Training-free AesTheTic-aware Outfit recOmmendation

Yuntian Wu, Xiaonan Hu, Ziqi Zhou +1

The global fashion e-commerce market relies significantly on intelligent and aesthetic-aware outfit-completion tools to promote sales. While previous studies have approached the pr…

cs.CV2025

HiTVideo: Hierarchical Tokenizers for Enhancing Text-to-Video Generation with Autoregressive Large Language Models

Ziqin Zhou, Yifan Yang, Yuqing Yang +7

Text-to-video generation poses significant challenges due to the inherent complexity of video data, which spans both temporal and spatial dimensions. It introduces additional redun…

cs.CV2024

EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance

Zicheng Duan, Yuxuan Ding, Chenhui Gou +3

Zero-shot personalized image generation models aim to produce images that align with both a given text prompt and subject image, requiring the model to incorporate both sources of…

cs.CV2024

Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale

Yangyang Shu, Xiaofeng Cao, Qi Chen +4

Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain d…