2 citations · 3 across the 12 of their papers we have counts for
14 papers
VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models
Dong Xing, Jiaxin Chen, Hang Yang +3
Large vision-language models (LVLMs) have demonstrated strong performance in open-ended video understanding, yet they remain prone to fluent responses unsupported by video evidence…
Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization
Liang Wang, Xinyi Mou, Xiaoyou Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, yet personalizing their outputs to individual users remains an open challenge. Existi…
Representation Forcing for Bottleneck-Free Unified Multimodal Models
Yuqing Wang, Zhijie Lin, Ceyuan Yang +10
Unified multimodal models (UMMs) aim to handle perception and generation in a single model. Yet existing UMMs still rely on a frozen, separately pretrained VAE for image generation…
MACRO: Advancing Multi-Reference Image Generation with Structured Long-Context Data
Zhekai Chen, Yuqing Wang, Manyuan Zhang +1
Generating images conditioned on multiple visual references is critical for real-world applications such as multi-subject composition, narrative illustration, and novel view synthe…
Cubic Discrete Diffusion: Discrete Visual Generation on High-Dimensional Representation Tokens
Yuqing Wang, Chuofan Ma, Zhijie Lin +7
Visual generation with discrete tokens has gained significant attention as it enables a unified token prediction paradigm shared with language models, promising seamless multimodal…
Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion
Yueming Pan, Ruoyu Feng, Qi Dai +5
Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. T…
Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation
Xiaoyu Yue, Zidong Wang, Yuqing Wang +5
Recent studies have demonstrated the importance of high-quality visual representations in image generation and have highlighted the limitations of generative models in image unders…
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning
Chengqi Duan, Rongyao Fang, Yuqing Wang +5
Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that specify multiple objects with precise…
SARTM: Segment Any RGB Thermal Model with Language aided Distillation
Dong Xing, Jinhe Zhang, Hang Yang +1
The recent Segment Anything Model (SAM) demonstrates strong instance segmentation performance across various downstream tasks. However, SAM is trained solely on RGB data, limiting…
Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation
Yuqing Wang, Zhijie Lin, Yao Teng +4
Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token repres…
Parallelized Autoregressive Visual Generation
Yuqing Wang, Shuhuai Ren, Zhijie Lin +6
Autoregressive models have emerged as a powerful approach for visual generation but suffer from slow inference speed due to their sequential token-by-token prediction process. In t…
LVD-2M: A Long-take Video Dataset with Temporally Dense Captions
Tianwei Xiong, Yuqing Wang, Daquan Zhou +3
The efficacy of video generation models heavily depends on the quality of their training datasets. Most previous video generation models are trained on short video clips, while rec…
Loong: Generating Minute-level Long Videos with Autoregressive Language Models
Yuqing Wang, Tianwei Xiong, Daquan Zhou +5
It is desirable but challenging to generate content-rich long videos in the scale of minutes. Autoregressive large language models (LLMs) have achieved great success in generating…
Similarity-aware Syncretic Latent Diffusion Model for Medical Image Translation with Representation Learning
Tingyi Lin, Pengju Lyu, Jie Zhang +3
Non-contrast CT (NCCT) imaging may reduce image contrast and anatomical visibility, potentially increasing diagnostic uncertainty. In contrast, contrast-enhanced CT (CECT) facilita…