1 citations · 1 across the 11 of their papers we have counts for
11 papers
X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving
Bohao Zhao, Chengrui Wei, Guangfeng Jiang +17
Predicting future states is essential for autonomous agents, yet current Vision-Language-Action (VLA) models fundamentally lack this capability, relying instead on reactive percept…
Test-Time Self-Adaptive Conditioning for Stable Audio-Driven Talking-Head Generation
Zhicheng Zhang, Lei Wang, Yu Zhang +1
Audio-driven talking-head generation has achieved remarkable progress with recent models such as AniTalker, FLOAT, and Sonic. Despite their success, most existing approaches rely o…
Selective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration
Moyu Zhang, Yun Chen, Yujun Jin +3
Scaling test-time compute has proven highly effective for language models, yet this opportunity remains largely unexplored for industrial Click-Through Rate (CTR) prediction. CTR m…
Self-Balancing Gradient Allocation for Heterogeneity-Aware Feature Generation in Click-Through Rate Prediction
Moyu Zhang, Yun Chen, Yujun Jin +3
Generative pre-training via discrete diffusion provides dense reconstruction supervision across all feature fields simultaneously, mitigating representation collapse from data spar…
X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling
Baolu Li, Jingyu Qian, Rui Guo +17
Physical world knowledge resides mainly in videos. Equipping Vision-Language-Action (VLA) models with such knowledge is fundamental for safe and generalizable planning. Predictive…
Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction
Moyu Zhang, Yujun Jin, Yun Chen +3
Generative models are increasingly being explored in click-through rate (CTR) prediction field to overcome the limitations of the conventional discriminative paradigm, which rely o…
MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths
Moyu Zhang, Yun Chen, Yujun Jin +3
Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining training objectives to aid parameter…
DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
Moyu Zhang, Yun Chen, Yujun Jin +2
Recent advances in generative models have inspired the field of recommender systems to explore generative approaches, but most existing research focuses on sequence generation, a p…
Global-Distribution Aware Scenario-Specific Variational Representation Learning Framework
Moyu Zhang, Yujun Jin, Jinxin Hu +1
With the emergence of e-commerce, the recommendations provided by commercial platforms must adapt to diverse scenarios to accommodate users' varying shopping preferences. Current m…
Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion
Moyu Zhang, Yongxiang Tang, Yujun Jin +2
Traditional recommendation methods rely on correlating the embedding vectors of item IDs to capture implicit collaborative filtering signals to model the user's interest in the tar…
IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers
Chenglin Yang, Siyuan Qiao, Yuan Cao +4
Generative training has been demonstrated to be powerful for building visual-language models. However, on zero-shot discriminative benchmarks, there is still a performance gap betw…