41 citations · 110 across the 20 of their papers we have counts for
22 papers
Towards Unbiased On-Policy Distillation for Block Diffusion Language Models
Zaiquan Yang, Fei Wei, Yong Wang +6
On-policy distillation (OPD) has emerged as an effective post-training paradigm for language models, with recent efforts extending it to block diffusion language models (BDLMs). Ho…
MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation
Ke Wang, Houxing Ren, Zimu Lu +4
End-to-end full-duplex speech models have brought open-source machine conversation closer to human-like interaction, yet existing systems remain limited in two intertwined dimensio…
LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving
Hao Shao, Letian Wang, Yang Zhou +5
Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck for large-scale deployment. To…
FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation
Zimu Lu, Houxing Ren, Yunqiao Yang +4
Assisting non-expert users to develop complex interactive websites has become a popular task for LLM-powered code agents. However, existing code agents tend to only generate fronte…
SlidesGen-Bench: Evaluating Slides Generation via Computational and Quantitative Metrics
Yunqiao Yang, Wenbo Li, Houxing Ren +6
The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts to image-centric synthesis. Howe…
DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving
Yang Zhou, Hao Shao, Letian Wang +3
Video generation models, as one form of world models, have emerged as one of the most exciting frontiers in AI, promising agents the ability to imagine the future by modeling the t…
DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation
Dongzhi Jiang, Renrui Zhang, Haodong Li +9
Recent unified multimodal large language models (MLLMs) have shown impressive capabilities, incorporating chain-of-thought (CoT) reasoning for enhanced text-to-image generation. Ho…
WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning
Zimu Lu, Houxing Ren, Yunqiao Yang +5
Agent systems powered by large language models (LLMs) have demonstrated impressive performance on repository-level code-generation tasks. However, for tasks such as website codebas…
T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
Dongzhi Jiang, Ziyu Guo, Renrui Zhang +6
Recent advancements in large language models have demonstrated how chain-of-thought (CoT) and reinforcement learning (RL) can improve performance. However, applying such reasoning…
ADT: Tuning Diffusion Models with Adversarial Supervision
Dazhong Shen, Guanglu Song, Yi Zhang +5
Diffusion models have achieved outstanding image generation by reversing a forward noising process to approximate true data distributions. During training, these models predict dif…
VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping
Hao Shao, Shulun Wang, Yang Zhou +7
Video face swapping is becoming increasingly popular across various applications, yet existing methods primarily focus on static images and struggle with video face swapping becaus…
EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM
Zhuofan Zong, Dongzhi Jiang, Bingqi Ma +5
Significant achievements in personalization of diffusion models have been witnessed. Conventional tuning-free methods mostly encode multiple reference images by averaging their ima…
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models
Bingqi Ma, Zhuofan Zong, Guanglu Song +2
Large language models (LLMs) based on decoder-only transformers have demonstrated superior text understanding capabilities compared to CLIP and T5-series models. However, the parad…
MoVA: Adapting Mixture of Vision Experts to Multimodal Context
Zhuofan Zong, Bingqi Ma, Dazhong Shen +5
As the key component in multimodal large language models (MLLMs), the ability of the visual encoder greatly affects MLLM's understanding on diverse image content. Although some lar…
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching
Dongzhi Jiang, Guanglu Song, Xiaoshi Wu +5
Diffusion models have demonstrated great success in the field of text-to-image generation. However, alleviating the misalignment between the text prompts and images is still challe…
Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning
Hao Shao, Shengju Qian, Han Xiao +5
Multi-Modal Large Language Models (MLLMs) have demonstrated impressive performance in various VQA tasks. However, they often lack interpretability and struggle with complex visual…
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Zeyue Xue, Guanglu Song, Qiushan Guo +4
Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images,…
Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction
Zhuofan Zong, Dongzhi Jiang, Guanglu Song +4
In this paper, we propose a new paradigm, named Historical Object Prediction (HoP) for multi-view 3D detection to leverage temporal information more effectively. The HoP approach i…
DETRs with Collaborative Hybrid Assignments Training
Zhuofan Zong, Guanglu Song, Yu Liu
In this paper, we provide the observation that too few queries assigned as positive samples in DETR with one-to-one set matching leads to sparse supervision on the encoder's output…
Large-batch Optimization for Dense Visual Predictions
Zeyue Xue, Jianming Liang, Guanglu Song +4
Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tac…
Self-slimmed Vision Transformer
Zhuofan Zong, Kunchang Li, Guanglu Song +4
Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bri…
RCNet: Reverse Feature Pyramid and Cross-scale Shift Network for Object Detection
Zhuofan Zong, Qianggang Cao, Biao Leng
Feature pyramid networks (FPN) are widely exploited for multi-scale feature fusion in existing advanced object detection frameworks. Numerous previous works have developed various…