10 citations · 14 across the 17 of their papers we have counts for
18 papers
Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation
Zhiheng Liu, Weiming Ren, Xiaoke Huang +12
Unified multimodal models typically rely on pretrained vision encoders and use separate visual representations for understanding and generation, creating misalignment between the t…
VecGlypher: Unified Vector Glyph Generation with Language Models
Xiaoke Huang, Bhavul Gauri, Kam Woh Ng +12
Vector glyphs are the atomic units of digital typography, yet most learning-based pipelines still depend on carefully curated exemplar sheets and raster-to-vector postprocessing, w…
OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory
Zhaochong An, Menglin Jia, Haonan Qiu +12
Storytelling in real-world videos often unfolds through multiple shots -- discontinuous yet semantically connected clips that together convey a coherent narrative. However, existin…
Scaling Zero-Shot Reference-to-Video Generation
Zijian Zhou, Shikun Liu, Haozhe Liu +14
Reference-to-video (R2V) generation aims to synthesize videos that align with a text prompt while preserving the subject identity from reference images. However, current R2V method…
TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models
Zhiheng Liu, Weiming Ren, Haozhe Liu +22
Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified conti…
Where on Earth? A Vision-Language Benchmark for Probing Model Geolocation Skills Across Scales
Zhaofang Qian, Hardy Chen, Zeyu Wang +9
Vision-language models (VLMs) have advanced rapidly, yet their capacity for image-grounded geolocation in open-world conditions, a task that is challenging and of demand in real li…
Synthesizing High-Quality Visual Question Answering from Medical Documents with Generator-Verifier LMMs
Xiaoke Huang, Ningsen Wang, Hui Liu +2
Large Multimodal Models (LMMs) are increasingly capable of answering medical questions that require joint reasoning over images and text, yet training general medical VQA systems i…
MedVLThinker: Simple Baselines for Multimodal Medical Reasoning
Xiaoke Huang, Juncheng Wu, Hui Liu +2
Large Reasoning Models (LRMs) have introduced a new paradigm in AI by enabling models to ``think before responding" via chain-of-thought reasoning. However, the absence of open and…
Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains
Juncheng Wu, Sheng Liu, Haoqin Tu +5
Recent advances in reasoning-enhanced Large Language Models such as OpenAI-o1/3 and DeepSeek-R1 have significantly improved performance on complex tasks. However, the quality and t…
m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models
Xiaoke Huang, Juncheng Wu, Hui Liu +2
Test-time scaling has emerged as a powerful technique for enhancing the reasoning capabilities of large language models. However, its effectiveness in medical reasoning remains unc…
Story-Iter: A Training-free Iterative Paradigm for Long Story Visualization
Jiawei Mao, Xiaoke Huang, Yunfei Xie +7
This paper introduces Story-Iter, a new training-free iterative paradigm to enhance long-story generation. Unlike existing methods that rely on fixed reference images to construct…
Medical Vision Generalist: Unifying Medical Imaging Tasks in Context
Sucheng Ren, Xiaoke Huang, Xianhang Li +5
This study presents Medical Vision Generalist (MVG), the first foundation model capable of handling various medical imaging tasks -- such as cross-modal synthesis, image segmentati…
VoCo-LLaMA: Towards Vision Compression with Large Language Models
Xubing Ye, Yukang Gan, Xiaoke Huang +2
Vision-Language Models (VLMs) have achieved remarkable success in various multi-modal tasks, but they are often bottlenecked by the limited context window and high computational co…
Segment and Caption Anything
Xiaoke Huang, Jianfeng Wang, Yansong Tang +5
We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything w…
Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution
Yiji Cheng, Fei Yin, Xiaoke Huang +5
Text-to-3D is an emerging task that allows users to create 3D content with infinite possibilities. Existing works tackle the problem by optimizing a 3D representation with guidance…
Efficient Meshy Neural Fields for Animatable Human Avatars
Xiaoke Huang, Yiji Cheng, Yansong Tang +3
Efficiently digitizing high-fidelity animatable human avatars from videos is a challenging and active research topic. Recent volume rendering-based neural representations open a ne…
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
Wanhua Li, Xiaoke Huang, Zheng Zhu +4
This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts.…
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
Wanhua Li, Xiaoke Huang, Jiwen Lu +2
Uncertainty is the only certainty there is. Modeling data uncertainty is essential for regression, especially in unconstrained settings. Traditionally the direct regression formula…