167 citations · 189 across the 13 of their papers we have counts for
15 papers
HLA: Expressive Hybrid Linear Attention via Chunk-Wise Dynamic Mixing
Zhuokun Chen, Xi Lin, Xiyu Wu +3
Linear attention enables efficient long-context autoregressive decoding by compressing history into recurrent states, but this compression can make selective access to sparse and d…
HLA-WM: Hybrid Linear Attention for Long-Horizon Video World Models
Zhuokun Chen, Feng Chen, Xi Lin +4
Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full generation history through a grow…
FlashBlock: Attention Caching for Efficient Long-Context Block Diffusion
Zhuokun Chen, Jianfei Cai, Bohan Zhuang
Generating long-form content, such as minute-long videos and extended texts, is increasingly important for modern generative models. Block diffusion improves inference efficiency v…
Where and What Matters: Sensitivity-Aware Task Vectors for Many-Shot Multimodal In-Context Learning
Ziyu Ma, Chenhui Gou, Yiming Hu +4
Large Multimodal Models (LMMs) have shown promising in-context learning (ICL) capabilities, but scaling to many-shot settings remains difficult due to limited context length and hi…
An Empirical Study on How Video-LLMs Answer Video Questions
Chenhui Gou, Ziyu Ma, Zicheng Duan +6
Taking advantage of large-scale data and pretrained language models, Video Large Language Models (Video-LLMs) have shown strong capabilities in answering video questions. However,…
R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning
Zhuokun Chen, Zeren Chen, Jiahao He +4
Chain-of-thought (CoT) enhances the problem-solving ability of large language models (LLMs) but incurs substantial inference cost due to long autoregressive trajectories. Existing…
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
Yuedong Chen, Chuanxia Zheng, Haofei Xu +4
We introduce MVSplat360, a feed-forward approach for 360° novel view synthesis (NVS) of diverse real-world scenes, using only sparse observations. This setting is inherently ill-po…
McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis
Sanuwani Dayarathna, Kh Tohidul Islam, Bohan Zhuang +4
Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis, offering diverse contrasts that provide comprehensive diagnostic information. However, acquiring multiple MR…
GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI
Pengcheng Chen, Jin Ye, Guoan Wang +15
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medic…
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Guoan Wang, Jin Ye, Junlong Cheng +5
Volumetric medical image segmentation is pivotal in enhancing disease diagnosis, treatment planning, and advancing medical research. While existing volumetric foundation models for…
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models
Jing Liu, Ruihao Gong, Mingyang Zhang +3
LLM development involves pre-training a foundation model on massive data, followed by fine-tuning on task-specific data to create specialized experts. Serving these experts can pos…
MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
Yuedong Chen, Haofei Xu, Chuanxia Zheng +5
We introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we bu…
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching
Zizheng Pan, Bohan Zhuang, De-An Huang +5
Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we int…
Efficient Stitchable Task Adaptation
Haoyu He, Zizheng Pan, Jing Liu +2
The paradigm of pre-training and fine-tuning has laid the foundation for deploying deep learning models. However, most fine-tuning methods are designed to meet a specific resource…
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
Jing Liu, Ruihao Gong, Xiuying Wei +3
Large Language Models (LLMs) excel in NLP, but their demands hinder their widespread deployment. While Quantization-Aware Training (QAT) offers a solution, its extensive training c…