1 citations · 1 across the 4 of their papers we have counts for
7 papers
UniAfford: Token-Routed Multitask Learning for Generalizable 2D-3D Affordance Perception
Yuhao Liu, Yiming Zhong, Hanqing Wang +9
Affordance perception aims to localize actionable regions supporting embodied interaction, yet 2D and 3D affordance grounding have evolved as separate problems, with different task…
Unbiased Top- Estimation for On-Policy Distillation
Linjian Meng, Siyuan Gan, YuHan Li +5
On-policy distillation (OPD) is becoming an important component of large language model (LLM) post-training for transferring the reasoning capability of a strong teacher LLM to a w…
CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization
Ziyang Ding, Linjian Meng, Yiming Wu +3
Due to the potential for exploratory reasoning of Latent Visual Reasoning, recent works tend to enable MLLMs (Multimodal Large Language Models) to perform visual reasoning by propa…
LLM-based Personalized Portfolio Recommender: Integrating Large Language Models and Reinforcement Learning for Intelligent Investment Strategy Optimization
Bangyu Li, Boping Gu, Ziyang Ding
In modern financial markets, investors increasingly seek personalized and adaptive portfolio strategies that reflect their individual risk preferences and respond to dynamic market…
SeqUDA-Rec: Sequential User Behavior Enhanced Recommendation via Global Unsupervised Data Augmentation for Personalized Content Marketing
Ruihan Luo, Xuanjing Chen, Ziyang Ding
Personalized content marketing has become a crucial strategy for digital platforms, aiming to deliver tailored advertisements and recommendations that match user preferences. Tradi…
An automatic patent literature retrieval system based on LLM-RAG
Yao Ding, Yuqing Wu, Ziyang Ding
With the acceleration of technological innovation efficient retrieval and classification of patent literature have become essential for intellectual property management and enterpr…
KARL: Knowledge-Aware Reasoning and Reinforcement Learning for Knowledge-Intensive Visual Grounding
Xinyu Ma, Ziyang Ding, Zhicong Luo +6
Knowledge-Intensive Visual Grounding (KVG) requires models to localize objects using fine-grained, domain-specific entity names rather than generic referring expressions. Although…