3 citations · 6 across the 15 of their papers we have counts for
19 papers
Unifying Distributional Training for One-Step Visual Generation
Chi Zhang, Shi Haoyang, Haoyang Shi +10
Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representation spaces. We introduce a unifi…
From Scores to Samples: Elastic Forcing for Autoregressive Video Generation
Chi Zhang, Yueyi Liu, Shi Haoyang +6
Few-step autoregressive video generation commonly relies on Distribution Matching Distillation (DMD), requiring a bidirectional diffusion teacher and an online fake-score model. We…
Rethinking VLM Representation for VLA Initialization
Weifeng Lin, Siyuan Huang, Hao Li +5
Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is…
Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with the MME-CoF Benchmark
Ziyu Guo, Xinyan Chen, Renrui Zhang +7
Recent video generation models can produce high-fidelity, temporally coherent videos, indicating that they may encode substantial world knowledge. Beyond realistic synthesis, they…
WoW: Towards a World omniscient World model Through Embodied Interaction
Xiaowei Chi, Peidong Jia, Chun-Kai Fan +33
Humans develop an understanding of intuitive physics through active interaction with the world. This approach is in stark contrast to current video models, such as Sora, which rely…
Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain
Yulin Luo, Chun-Kai Fan, Menghang Dong +19
Building robots that can perceive, reason, and act in dynamic, unstructured environments remains a central challenge. Recent embodied systems often follow a dual-system paradigm, w…
Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos
Weifeng Lin, Xinyu Wei, Ruichuan An +7
We present Perceive Anything Model (PAM), a conceptually straightforward and efficient framework for comprehensive region-level visual understanding in images and videos. Our appro…
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
Pengxiang Li, Shilin Yan, Joey Tsai +4
Classifier-Free Guidance (CFG) significantly enhances controllability in generative models by interpolating conditional and unconditional predictions. However, standard CFG often e…
SpikeGen: Decoupled "Rods and Cones" Visual Representation Processing with Latent Generative Framework
Gaole Dai, Menghang Dong, Rongyu Zhang +3
The process through which humans perceive and learn visual representations in dynamic environments is highly complex. From a structural perspective, the human eye decouples the fun…
UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens
Ruichuan An, Sihan Yang, Renrui Zhang +10
Personalized models have demonstrated remarkable success in understanding and generating concepts provided by users. However, existing methods use separate concept tokens for under…
LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts
Qifeng Cai, Hao Liang, Zhaoyang Han +6
Long videos contain a vast amount of information, making video-text retrieval an essential and challenging task in multimodal learning. However, existing benchmarks suffer from lim…
MC-LLaVA: Multi-Concept Personalized Vision-Language Model
Ruichuan An, Sihan Yang, Ming Lu +9
Current vision-language models (VLMs) show exceptional abilities across diverse tasks, such as visual question answering. To enhance user experience, recent studies investigate VLM…
MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders
Jiajun Cao, Yuan Zhang, Tao Huang +5
Visual encoders are fundamental components in vision-language models (VLMs), each showcasing unique strengths derived from various pre-trained visual foundation models. To leverage…
MC-LLaVA: Multi-Concept Personalized Vision-Language Model
Ruichuan An, Sihan Yang, Renrui Zhang +10
Current vision-language models (VLMs) show exceptional abilities across diverse tasks, such as visual question answering. To enhance user experience, recent studies have investigat…
Can Modifying Data Address Graph Domain Adaptation?
Renhong Huang, Jiarong Xu, Xin Jiang +2
Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to d…
LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model
Yulin Luo, Ruichuan An, Bocheng Zou +3
The distribution of subpopulations is an important property hidden within a dataset. Uncovering and analyzing the subpopulation distribution within datasets provides a comprehensiv…
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
Weifeng Lin, Xinyu Wei, Ruichuan An +6
In this paper, we present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs). V…
MoEC: Mixture of Experts Implicit Neural Compression
Jianchen Zhao, Cheng-Ching Tseng, Ming Lu +4
Emerging Implicit Neural Representation (INR) is a promising data compression technique, which represents the data using the parameters of a Deep Neural Network (DNN). Existing met…
MoSA: Mixture of Sparse Adapters for Visual Efficient Tuning
Qizhe Zhang, Bocheng Zou, Ruichuan An +2
With the rapid growth in the scale of pre-trained foundation models, parameter-efficient fine-tuning techniques have gained significant attention, among which Adapter Tuning is the…