most citedMARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake

Ruicheng Zhang, Jianhui Lei, Kaiwen Shen +6

Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-level misclassification issues suc…

cs.LG2026

When Should the Teacher Move? Temporal Coupling and Stability in Self On-Policy Distillation

Haowei Guo, Baolong Bi, Ruicheng Zhang +4

Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- which governs the \emph{temporal…

cs.CV2026

LongCat-Next: Lexicalizing Modalities as Discrete Tokens

Meituan LongCat Team, Bin Xiao, Chao Wang +86

The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…

cs.CV2025★ 1 cited

MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation

Ruicheng Zhang, Yu Sun, Zeyu Zhang +5

We introduce MARL-MambaContour, the first contour-based medical image segmentation framework based on Multi-Agent Reinforcement Learning (MARL). Our approach reframes segmentation…

cs.CV2025★ 1 cited

Unified Medical Image Segmentation with State Space Modeling Snake

Ruicheng Zhang, Haowei Guo, Kanghui Tian +4

Unified Medical Image Segmentation (UMIS) is critical for comprehensive anatomical assessment but faces challenges due to multi-scale structural heterogeneity. Conventional pixel-b…

cs.CV2025

GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation

Ruicheng Zhang, Haowei Guo, Zeyu Zhang +2

Multi-organ segmentation is a critical yet challenging task due to complex anatomical backgrounds, blurred boundaries, and diverse morphologies. This study introduces the Gradient-…