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Weidong Wang

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13 papers
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13

AAAI Conference 2026 Conference Paper

E³SAM2: Entropy-Aware and Edge-Guided Adaptation of SAM2 for Echocardiography Video Segmentation

  • Long Zheng
  • Zhi Li
  • Weidong Wang
  • Zhenyu Dai
  • Shuyun Li

Foundation segmentation models, such as SAM and its video-oriented variant SAM2, have achieved remarkable success in natural image and video segmentation. However, their direct application to echocardiography video is challenged by structural uncertainty arising from severe speckle noise and blurry anatomical boundaries. To address this, we propose E³SAM2, a lightweight adaptation framework that introduces a novel entropy-based methodology to explicitly model and mitigate such uncertainty. Specifically, an entropy-guided attention mechanism is introduced to steer the model’s focus toward structurally reliable features, particularly in speckle-dominated regions. Additionally, an entropy regularization loss is introduced to further enhance target-background discrimination. To better resolve indistinct anatomical contours, an edge-aware supervision module is incorporated to inject explicit boundary priors for sharper delineation. These components are efficiently integrated through a global-local feature adapter. Experiments on CAMUS and EchoNet-Dynamic datasets demonstrate that E³SAM2 achieves state-of-the-art segmentation and clinical estimation performance, while maintaining high computational efficiency.

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ICML Conference 2025 Conference Paper

EPIC: Efficient Position-Independent Caching for Serving Large Language Models

  • Junhao Hu
  • Wenrui Huang
  • Weidong Wang
  • Haoyi Wang
  • Tiancheng Hu
  • Qin Zhang
  • Hao Feng
  • Xusheng Chen

Large Language Models (LLMs) show great capabilities in a wide range of applications, but serving them efficiently becomes increasingly challenging as requests (prompts) become more complex. Context caching improves serving performance by reusing Key-Value (KV) vectors, the intermediate representations of tokens that are repeated across requests. However, existing context caching requires exact prefix matches across requests, limiting reuse cases in settings such as few-shot learning and retrieval-augmented generation, where immutable content (e. g. , documents) remains unchanged across requests but is preceded by varying prefixes. Position-Independent Caching (PIC) addresses this issue by enabling modular reuse of the KV vectors regardless of prefixes. We formalize PIC and advance prior work by introducing EPIC, a serving system incorporating our new LegoLink algorithm, which mitigates the inappropriate “attention sink” effect at every document beginning, to maintain accuracy with minimal computation. Experiments show that EPIC achieves up to 8$\times$ improvements in Time-To-First-Token (TTFT) and 7$\times$ throughput gains over existing systems, with negligible or no accuracy loss.

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IROS Conference 2021 Conference Paper

DSVP: Dual-Stage Viewpoint Planner for Rapid Exploration by Dynamic Expansion

  • Hongbiao Zhu
  • Chao Cao
  • Yukun Xia
  • Sebastian A. Scherer
  • Ji Zhang 0003
  • Weidong Wang

We present a method for efficiently exploring highly convoluted environments. The method incorporates two planning stages - an exploration stage for extending the boundary of the map, and a relocation stage for explicitly transiting the robot to different sub-areas in the environment. The exploration stage develops a local Rapidly-exploring Random Tree (RRT) in the free space of the environment, and the relocation stage maintains a global graph through the mapped environment, both are dynamically expanded over replanning steps. The method is compared to existing state-of-the-art methods in various challenging simulation and real environments. Experiment comparisons show that our method is twice as efficient in exploring spaces using less processing than the existing methods. Further, we release a benchmark environment to evaluate exploration algorithms as well as facilitate development of autonomous navigation systems. The benchmark environment and our method are open-sourced.

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