Jian Dong Zhang — accepted AI-conference papers

2026

High-Fidelity Virtual Try-On beyond Paired Data Scarcity via Diffusion-based Cycle-Consistent Learning

Jia Wu, Yijing Dai, Tingfeng Cao, Meiling Wu, Tao Luo, Jian Dong Zhang +2

CVPR 2026

Diffusion-based virtual try-on methods rely on vast high-quality garment-person pairs, which are scarce in practice due to the high cost of data collection and preprocessing, limiting their performance in real-world scenarios.To overcome this bottleneck, we propose Cycle-Consistent Virtual Try-On (C

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2026

ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents

Jiangyuan Wang, Kejun Xiao, Qi Sun, Huaipeng Zhao, Tao Luo, Jian Dong Zhang +1

AAAI 2026technical

Existing benchmarks in e-commerce primarily focus on basic user intents, such as finding or purchasing products. However, real-world users often pursue more complex goals, such as applying vouchers, managing budgets, and finding multi-products seller. To bridge this gap, we propose ShoppingBench, a

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