1 citations · 2 across the 29 of their papers we have counts for
34 papers
CMI-Mem: Toward Generalizable Long-Term Memory Management via CMI-Augmented Reinforcement Learning
Yubo Wang, Qiuyu Zhao, Zenghui Sun +6
Memory Manager models are pivotal in agent systems. Existing reinforcement-learning methods commonly use LLM-judged synthetic question-answer (QA) pairs: this provides useful downs…
ReCap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning
Haonan Jia, Shichao Dong, Zenghui Sun +7
Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel rea…
ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesis
Yuan Wang, Yongchao Du, Mengting Chen +4
Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However,…
MMShopBench: A Real-Log Benchmark for Multimodal, Multi-Turn Shopping Agents
Zeying Hao, Hao Guo, Mengtao Xu +5
Online shoppers increasingly turn to AI shopping assistants, using images and multi-turn dialogue to express and refine product needs that are difficult to articulate in text alone…
TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy
Hao Sun, Hao Yan, Mengting Chen +7
While Video Virtual Try-on (VVT) has achieved remarkable progress in synthesizing realistic garment overlays on dynamic subjects, existing paradigms remains fundamentally constrain…
Pailitao-MMSearch: Building Native E-Commerce Multimodal Search Foundation
Xiaohan Ye, Xu Chen, Zihan Gong +15
The evolution of e-commerce has fundamentally transformed how users search for products, shifting from simple text-based keyword queries to complex multimodal interactions that sea…
iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance
Jun Zheng, Zhengze Xu, Mengting Chen +6
Video Virtual Try-On (VVT) aims to seamlessly replace a garment on a person in a video with a new one. While existing methods have made significant strides in maintaining temporal…
FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization
Quanjian Song, Yefeng Shen, Mengting Chen +5
Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot support low-latency and interactiv…
SkillChain: Closing the Loop on Skill Evolution for Image-Based E-Commerce AI Assistants
Yimin Hu, Mengtao Xu, Hao Guo +3
Image-based AI assistants are now deployed at production scale on e-commerce platforms, where a single uploaded image can trigger fundamentally different user intents: product sear…
Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items
Mengting Chen, Zhengrui Chen, Yongchao Du +16
Recent advances in image generation and editing have opened new opportunities for virtual try-on. However, existing methods still struggle to meet complex real-world demands. We pr…
What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion
Zhengrong Yue, Taihang Hu, Mengting Chen +8
Tokenizers are a crucial component of latent diffusion models, as they define the latent space in which diffusion models operate. However, existing tokenizers are primarily designe…
Continuous-Time Distribution Matching for Few-Step Diffusion Distillation
Tao Liu, Hao Yan, Mengting Chen +8
Step distillation has become a leading technique for accelerating diffusion models, among which Distribution Matching Distillation (DMD) and Consistency Distillation are two repres…
Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning
Haonan Jia, Shichao Dong, Xin Dong +6
Large Vision-Language Models (LVLMs) often omit or misrepresent critical visual content in generated image captions. Minimizing such information loss will force LVLMs to focus on i…
QuadSentinel: Sequent Safety for Machine-Checkable Control in Multi-agent Systems
Yiliu Yang, Yilei Jiang, Qunzhong Wang +5
Safety risks arise as large language model-based agents solve complex tasks with tools, multi-step plans, and inter-agent messages. However, deployer-written policies in natural la…
Online-PVLM: Advancing Personalized VLMs with Online Concept Learning
Huiyu Bai, Runze Wang, Zhuoyun Du +6
Personalized Visual Language Models (VLMs) are gaining increasing attention for their formidable ability in user-specific concepts aligned interactions (e.g., identifying a user's…
ConceptGuard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection
Ruize Ma, Minghong Cai, Yilei Jiang +7
Recent progress in video generative models has enabled the creation of high-quality videos from multimodal prompts that combine text and images. While these systems offer enhanced…
Enhancing Sequential Recommendation with World Knowledge from Large Language Models
Tianjie Dai, Xu Chen, Yunmeng Shu +4
Sequential Recommendation System~(SRS) has become pivotal in modern society, which predicts subsequent actions based on the user's historical behavior. However, traditional collabo…
Enabling Agents to Communicate Entirely in Latent Space
Zhuoyun Du, Runze Wang, Huiyu Bai +6
While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling rich, internal latent states int…
REVISION:Reflective Intent Mining and Online Reasoning Auxiliary for E-commerce Visual Search System Optimization
Yiwen Tang, Qiuyu Zhao, Zenghui Sun +3
In Taobao e-commerce visual search, user behavior analysis reveals a large proportion of no-click requests, suggesting diverse and implicit user intents. These intents are expresse…
Leveraging Scene Context with Dual Networks for Sequential User Behavior Modeling
Xu Chen, Yunmeng Shu, Yuangang Pan +6
Modeling sequential user behaviors for future behavior prediction is crucial in improving user's information retrieval experience. Recent studies highlight the importance of incorp…
INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling
Xin Dong, Shichao Dong, Jin Wang +7
Hallucinations in large vision-language models (LVLMs) pose significant challenges for real-world applications, as LVLMs may generate responses that appear plausible yet remain inc…
PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization
Zouying Cao, Runze Wang, Yifei Yang +4
Large Language Model (LLM) agents have demonstrated impressive capabilities in handling complex interactive problems. Existing LLM agents mainly generate natural language plans to…
MSR-Align: Policy-Grounded Multimodal Alignment for Safety-Aware Reasoning in Vision-Language Models
Yinan Xia, Yilei Jiang, Yingshui Tan +3
Vision-Language Models (VLMs) have achieved remarkable progress in multimodal reasoning tasks through enhanced chain-of-thought capabilities. However, this advancement also introdu…
USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models
Baolin Zheng, Guanlin Chen, Hongqiong Zhong +12
Despite their remarkable achievements and widespread adoption, Multimodal Large Language Models (MLLMs) have revealed significant security vulnerabilities, highlighting the urgent…