most citedTIIF-Bench: How Does Your T2I Model Follow Your Instructions?

1 citations · 1 across the 15 of their papers we have counts for

collaborators

16 papers

cs.IR2026

MuSeR: Scalable Long-sequence Recommendation with Multi-interest Modeling

Yongkang Fu, Beining Bao, Yu Jiang +10

Ultra-long user behavior sequences carry rich signals of stable and diverse preferences, yet industrial recommender systems typically truncate histories to a few hundred actions un…

cs.CV2026

SpatialFlow-GRPO: Where Spatial Credit Drives Image Editing

Yankai Yang, Yancheng Long, Wei Chen +7

Recent online reinforcement learning has substantially improved image editing quality. However, existing Flow-GRPO-style methods usually rely on a single whole-image reward, which…

cs.CV2026

Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction

Shannan Yan, Leqi Zheng, Keyu Lv +7

We study the task of establishing object-level visual correspondence across different viewpoints in videos, focusing on the challenging egocentric-to-exocentric and exocentric-to-e…

cs.CV2026

A Neuro-Symbolic Framework Combining Inductive and Deductive Reasoning for Autonomous Driving Planning

Hongyan Wei, Wael AbdAlmageed

Existing end-to-end autonomous driving models rely heavily on purely data-driven inductive reasoning. This "black-box" nature leads to a lack of interpretability and absolute safet…

cs.CV2026

UniRef-Image-Edit: Towards Scalable and Consistent Multi-Reference Image Editing

Hongyang Wei, Bin Wen, Yancheng Long +22

We present UniRef-Image-Edit, a high-performance multi-modal generation system that unifies single-image editing and multi-image composition within a single framework. Existing dif…

cs.CV2026

Spatial Chain-of-Thought: Bridging Understanding and Generation Models for Spatial Reasoning Generation

Wei Chen, Yancheng Long, Mingqiao Liu +9

While diffusion models have shown exceptional capabilities in aesthetic image synthesis, they often struggle with complex spatial understanding and reasoning. Existing approaches r…

cs.AI2026

Joint Reward Modeling: Internalizing Chain-of-Thought for Efficient Visual Reward Models

Yankai Yang, Yancheng Long, Hongyang Wei +12

Reward models are critical for reinforcement learning from human feedback, as they determine the alignment quality and reliability of generative models. For complex tasks such as i…

cs.CV2026

SpatialReward: Bridging the Perception Gap in Online RL for Image Editing via Explicit Spatial Reasoning

Yancheng Long, Yankai Yang, Hongyang Wei +12

Online Reinforcement Learning (RL) offers a promising avenue for complex image editing but is currently constrained by the scarcity of reliable and fine-grained reward signals. Exi…

cs.CV2026

Skywork UniPic 3.0: Unified Multi-Image Composition via Sequence Modeling

Hongyang Wei, Hongbo Liu, Zidong Wang +11

The recent surge in popularity of Nano-Banana and Seedream 4.0 underscores the community's strong interest in multi-image composition tasks. Compared to single-image editing, multi…

cs.CV2026

Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation

Zihan Su, Hongyang Wei, Kangrui Cen +4

Unified Multimodal Models (UMMs) integrate both visual understanding and generation within a single framework. Their ultimate aspiration is to create a cycle where understanding an…

cs.CV2025

MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition

Xinyu Wei, Kangrui Cen, Hongyang Wei +6

In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., Multi-Image Composition (MICo), remains a challenging problem, p…

cs.CV2025

VideoVerse: Does Your T2V Generator Have World Model Capability to Synthesize Videos?

Zeqing Wang, Xinyu Wei, Bairui Li +5

The recent rapid advancement of Text-to-Video (T2V) generation technologies are engaging the trained models with more world model ability, making the existing benchmarks increasing…

cs.CV2025

Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model

Hongyang Wei, Baixin Xu, Hongbo Liu +18

Recent advances in multimodal models have demonstrated impressive capabilities in unified image generation and editing. However, many prominent open-source models prioritize scalin…

cs.CV2025

Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding and Generation

Peiyu Wang, Yi Peng, Yimeng Gan +12

We introduce Skywork UniPic, a 1.5 billion-parameter autoregressive model that unifies image understanding, text-to-image generation, and image editing within a single architecture…

cs.CV2025★ 1 cited

TIIF-Bench: How Does Your T2I Model Follow Your Instructions?

Xinyu Wei, Jinrui Zhang, Zeqing Wang +4

The rapid advancements of Text-to-Image (T2I) models have ushered in a new phase of AI-generated content, marked by their growing ability to interpret and follow user instructions.…

cs.CV2025

Perceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative Models

Hongyang Wei, Shuaizheng Liu, Chun Yuan +1

By leveraging the generative priors from pre-trained text-to-image diffusion models, significant progress has been made in real-world image super-resolution (Real-ISR). However, th…