2 citations · 6 across the 13 of their papers we have counts for
13 papers
SuperFlow: Training Flow Matching Models with RL on the Fly
Kaijie Chen, Zhiyang Xu, Ying Shen +3
Recent progress in flow-based generative models and reinforcement learning (RL) has improved text-image alignment and visual quality. However, current RL training for flow models s…
LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer
Ying Shen, Zhiyang Xu, Jiuhai Chen +6
Recent advances in multimodal foundation models unifying image understanding and generation have opened exciting avenues for tackling a wide range of vision-language tasks within a…
R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation
Kaijie Chen, Zihao Lin, Zhiyang Xu +5
Reasoning is a fundamental capability often required in real-world text-to-image (T2I) generation, e.g., generating ``a bitten apple that has been left in the air for more than a w…
LLM Braces: Straightening Out LLM Predictions with Relevant Sub-Updates
Ying Shen, Lifu Huang
Recent findings reveal that much of the knowledge in a Transformer-based Large Language Model (LLM) is encoded in its feed-forward (FFN) layers, where each FNN layer can be interpr…
SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Models
Yue Zhang, Zhiyang Xu, Ying Shen +2
Integrating the 3D world into large language models (3D-based LLMs) has been a promising research direction for 3D scene understanding. However, current 3D-based LLMs fall short in…
Modality-Specialized Synergizers for Interleaved Vision-Language Generalists
Zhiyang Xu, Minqian Liu, Ying Shen +5
Recent advancements in Vision-Language Models (VLMs) have led to the emergence of Vision-Language Generalists (VLGs) capable of understanding and generating both text and images. H…
InternalInspector : Robust Confidence Estimation in LLMs through Internal States
Mohammad Beigi, Ying Shen, Runing Yang +7
Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinat…
Many-to-many Image Generation with Auto-regressive Diffusion Models
Ying Shen, Yizhe Zhang, Shuangfei Zhai +3
Recent advancements in image generation have made significant progress, yet existing models present limitations in perceiving and generating an arbitrary number of interrelated ima…
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
Zhiyang Xu, Chao Feng, Rulin Shao +6
Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking ta…
Multimodal Instruction Tuning with Conditional Mixture of LoRA
Ying Shen, Zhiyang Xu, Qifan Wang +3
Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot g…
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects
Minqian Liu, Ying Shen, Zhiyang Xu +5
Natural Language Generation (NLG) typically involves evaluating the generated text in various aspects (e.g., consistency and naturalness) to obtain a comprehensive assessment. Howe…
MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks
Jingyuan Qi, Minqian Liu, Ying Shen +2
Automatically generating scripts (i.e. sequences of key steps described in text) from video demonstrations and reasoning about the subsequent steps are crucial to the modern AI vir…
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
Jingyuan Qi, Zhiyang Xu, Ying Shen +4
Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps. However, confined by its inherent single-pass a…