3 citations · 3 across the 13 of their papers we have counts for
9 papers · 1 filter
TryOnReward: Learning Foveated Consistency for Reinforcement Fine-Tuning of Virtual Try-On
Xueheng Li, Yong Liu, Xiaolong Fu +5
Virtual Try-On (VTON) aims to dress a person with the reference garment, producing visually reasonable results aligned with human preferences. Turning this preference-oriented goal…
Is Reasoning Always Useful? Rethinking Reasoning Utility in Universal Multimodal Embeddings
Wenxiao Fan, Jingling Fu, Luohang Liu +6
Reasoning-enhanced universal multimodal embeddings (UME) improve heterogeneous retrieval, but plausible rationales do not necessarily produce discriminative rankings. We study this…
Match One, Learn with Graph: One-to-Graph Query Collaboration with Backward Sharing for Object Detection
Wenxiao Fan, Jingling Fu, Luohang Liu +8
One-to-one (O2O) matching enables Detection Transformers (DETRs) to perform end-to-end set prediction by assigning each object to a single positive query. However, the strongest cl…
Recompute or Reuse? Diagnosing and Mitigating Textual Shortcuts in VLM Self-Reflection
Wenxiao Fan, Jingling Fu, Fang Li +9
Vision-language models (VLMs) are expected to revise their reasoning when visual evidence changes. Failures to do so are often attributed to insufficient visual attention or contex…
Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On
Yong Liu, Xiaolong Fu, Zihang Xu +10
We present Oxygen-TryOn, a unified foundation model for any-item virtual try-on. Rather than repurposing a general-purpose image editor, Oxygen-TryOn is fashion-native, built for t…
GMO-EDIT: Grounded Multi-Operation Editing for E-Commerce Images
Zipeng Guo, Xiaoan Liu, Lichen Ma +9
Real-world e-commerce image editing often requires multiple, localized, and auditable operations rather than global restyling. This compositional nature poses a dual challenge: mod…
PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion
Zipeng Guo, Lichen Ma, Yu He +4
End-to-end pixel-space diffusion models bypass the lossy compression of Latent Diffusion Models (LDMs) but struggle to jointly model low-frequency semantics and high-frequency sign…
HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion
Yu He, Lichen Ma, Zipeng Guo +5
Pixel-space diffusion models bypass the reconstruction bottleneck of Variational Autoencoders (VAEs) but face a fundamental "granularity dilemma": capturing global semantics favors…
FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion
Lichen Ma, Zipeng Guo, Yu He +5
To circumvent the inherent fidelity bottlenecks and optimization misalignment of VAE-based latent diffusion, pixel-space diffusion models have emerged as a compelling end-to-end pa…