2 citations · 2 across the 11 of their papers we have counts for
10 papers · 1 filter
Background Gradients Shape Memorization in Flow Matching
Xuanhua Yin, Boyu Wei, Shuyi Zhang +3
Repetition is closely associated with memorization in generative models, but how other training images affect the retention and copying of targets remains unclear. We study this qu…
ReAL: Accelerating Flow Matching through Segment Advancement with Shared Lookahead
Xuanhua Yin, Chuanzhi Xu, Haoxian Zhou +2
Flow-matching models generate high-quality images and videos, but repeated neural network evaluations make sampling expensive. Skipping evaluations reduces this cost by extending a…
DefaultShift: Auditing Semantic Default Shift in Accelerated Text-to-Image Models
Xuanhua Yin, Chuanzhi Xu, Shunqi Mao +2
Few-step text-to-image models increasingly replace slower generators, yet acceleration can silently change distributions over unspecified attributes even when individual outputs re…
Calibrate What You SHIP: Post-Selection Risk Control for Verifier-Guided Text-to-Image Generation
Xuanhua Yin, Shunqi Mao, Wei Guo +2
Verifier-guided text-to-image systems increasingly use test-time search to select, refine, or stop among multiple candidates, yet release thresholds are often calibrated on individ…
Collapse of Patches: Ranking Image Patches for Efficient Visual Modeling
Wei Guo, Shunqi Mao, Zhuonan Liang +3
Observing certain patches in an image reduces the uncertainty of others. Their realization lowers the distribution entropy of each remaining patch feature, analogous to collapsing…
Beyond Random Masking: A Dual-Stream Approach for Rotation-Invariant Point Cloud Masked Autoencoders
Xuanhua Yin, Dingxin Zhang, Yu Feng +3
Existing rotation-invariant point cloud masked autoencoders (MAE) rely on random masking strategies that overlook geometric structure and semantic coherence. Random masking treats…
Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations
Shunqi Mao, Wei Guo, Chaoyi Zhang +3
Diffusion models generate conditional samples by progressively denoising Gaussian noise, yet the denoising trajectory can stall at visually plausible but low-quality outcomes with…
Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding
Shunqi Mao, Chaoyi Zhang, Weidong Cai
Existing vision-language models (VLMs) often suffer from visual hallucination, where the generated responses contain inaccuracies that are not grounded in the visual input. Efforts…
Controllable Contextualized Image Captioning: Directing the Visual Narrative through User-Defined Highlights
Shunqi Mao, Chaoyi Zhang, Hang Su +3
Contextualized Image Captioning (CIC) evolves traditional image captioning into a more complex domain, necessitating the ability for multimodal reasoning. It aims to generate image…
DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference
Zhihao Shuai, Yinan Chen, Shunqiang Mao +2
Weakly supervised medical image segmentation (MIS) using generative models is crucial for clinical diagnosis. However, the accuracy of the segmentation results is often limited by…