18 papers
Pixel-Space Diffusion via Observation Operators
Shaojie Guo, Lichen Ma, Haoyang Tong +8
Pixel-space diffusion models directly model image distributions but remain difficult to optimize. Recent methods alleviate this challenge through target reparameterization, while s…
PosterText: Towards Unified Visual Text Generation and Editing for E-commerce Poster
Xiaoan Liu, Lichen Ma, Zipeng Guo +12
Automated e-commerce poster design requires both high-quality poster generation and flexible editing of existing designs. However, most existing methods either target end-to-end po…
TransAnyText: Translating Arbitrary Text in E-commerce Images via Structured Visual Generation
Xiaoan Liu, Lichen Ma, Zipeng Guo +13
Cross-border e-commerce image translation is essential for global retail, where product images, banners, and detail pages need to be produced in different languages. Existing metho…
Energy-Guided Flow Matching
Haoyang Tong, Yu He, Fang Li +6
Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flo…
iFAN: Inference-Aware Learning for Plain Mask Transformers
Fang Li, Yu He, Haoyang Tong +7
Query-based mask transformers assemble segmentation outputs through pixel-wise competition among query predictions of the final layer, yet this inference process is not explicitly…
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…
Rethinking Calibration for High-Sparsity LLM Pruning: From Single Sources to Capability Coverage
Hu Xu, Zhaolong Xing, Congcong Liu +5
Post-training LLM pruning relies on a small calibration set, usually chosen heuristically and judged by WikiText perplexity or by mean accuracy on general-domain commonsense benchm…
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…
LiWi: Layering in the Wild
Yu He, Fang Li, Haoyang Tong +7
Recent advances in generative models have empowered impressive layered image generation, yet their success is largely confined to graphic design domains. The layering of in-the-wil…
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…
Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition
Yu He, Ting Zhu, Yichun Liu +6
Recent research work on fashion outfit generation focuses on promoting visual consistency of garments by leveraging key information from reference image and text prompt. However, t…
Meta-TTRL: A Metacognitive Framework for Self-Improving Test-Time Reinforcement Learning in Unified Multimodal Models
Lit Sin Tan, Junzhe Chen, Xiaolong Fu +5
Existing test-time scaling (TTS) methods for unified multimodal models (UMMs) in text-to-image (T2I) generation primarily rely on search or sampling strategies that produce only in…
UM-Text: A Unified Multimodal Model for Image Understanding and Visual Text Editing
Lichen Ma, Xiaolong Fu, Gaojing Zhou +6
With the rapid advancement of image generation, visual text editing using natural language instructions has received increasing attention. The main challenge of this task is to ful…
RePainter: Empowering E-commerce Object Removal via Spatial-matting Reinforcement Learning
Zipeng Guo, Lichen Ma, Xiaolong Fu +13
In web data, product images are central to boosting user engagement and advertising efficacy on e-commerce platforms, yet the intrusive elements such as watermarks and promotional…
Dynamic-TreeRPO: Breaking the Independent Trajectory Bottleneck with Structured Sampling
Xiaolong Fu, Lichen Ma, Zipeng Guo +9
The integration of Reinforcement Learning (RL) into flow matching models for text-to-image (T2I) generation has driven substantial advances in generation quality. However, these ga…