27 citations · 60 across the 14 of their papers we have counts for
15 papers
IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems
A. Jian Yang, An Huang, Bo Dan +173
We present IQuest-Q1, a sparse Mixture-of-Experts (MoE) agentic foundation model with 320B total parameters (15B activated). IQuest-Q1 is developed through a three-stage process: (…
What Should a Skill Remember? Quality--Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents
Qinghua Xing, Yinda Chen, Yaping Jin +6
Large language model agents increasingly rely on skills: reusable procedural documents encoding workflows, tool use, implementation patterns, validation checks, and domain rules. S…
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation
Jinpeng Lu, Linghan Cai, Yinda Chen +4
Lightweight 3D medical image segmentation remains constrained by a fundamental \textit{``efficiency / robustness conflict''}, particularly when processing complex anatomical struct…
Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration
Che Liu, Yinda Chen, Haoyuan Shi +21
The advent of large-scale vision foundation models, pre-trained on diverse natural images, has marked a paradigm shift in computer vision. However, how the frontier vision foundati…
Dual form Complementary Masking for Domain-Adaptive Image Segmentation
Jiawen Wang, Yinda Chen, Xiaoyu Liu +4
Recent works have correlated Masked Image Modeling (MIM) with consistency regularization in Unsupervised Domain Adaptation (UDA). However, they merely treat masking as a special fo…
Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression
Siqi Wu, Yinda Chen, Dong Liu +1
In this paper, we study how to synthesize a dynamic reference from an external dictionary to perform conditional coding of the input image in the latent domain and how to learn the…
QMamba: Post-Training Quantization for Vision State Space Models
Yinglong Li, Xiaoyu Liu, Jiacheng Li +3
State Space Models (SSMs), as key components of Mamaba, have gained increasing attention for vision models recently, thanks to their efficient long sequence modeling capability. Gi…
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
Che Liu, Zhongwei Wan, Haozhe Wang +6
Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically r…
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation
Runzhao Yang, Yinda Chen, Zhihong Zhang +6
In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are co…
TokenUnify: Scaling Up Autoregressive Pretraining for Neuron Segmentation
Yinda Chen, Haoyuan Shi, Xiaoyu Liu +5
Neuron segmentation from electron microscopy (EM) volumes is crucial for understanding brain circuits, yet the complex neuronal structures in high-resolution EM images present sign…
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
Yinda Chen, Che Liu, Xiaoyu Liu +2
The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals. To assist clinicians in their diagnosti…
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency
Che Liu, Cheng Ouyang, Yinda Chen +7
While 3D visual self-supervised learning (vSSL) shows promising results in capturing visual representations, it overlooks the clinical knowledge from radiology reports. Meanwhile,…
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning
Yinda Chen, Wei Huang, Shenglong Zhou +2
The performance of existing supervised neuron segmentation methods is highly dependent on the number of accurate annotations, especially when applied to large scale electron micros…
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation
Yinda Chen, Wei Huang, Xiaoyu Liu +3
Instance segmentation in electron microscopy (EM) volumes is tough due to complex shapes and sparse annotations. Self-supervised learning helps but still struggles with intricate v…
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation
Yinda Chen, Che Liu, Wei Huang +3
Vision-Language Pretraining (VLP) has demonstrated remarkable capabilities in learning visual representations from textual descriptions of images without annotations. Yet, effectiv…