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Yulin Li

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7 papers
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7

JBHI Journal 2026 Journal Article

Waveformer: Dual-Branch Adaptive Network with Wavelet-Guided Cross-Context Decoding for Colorectal Polyp Segmentation

  • Liming Liang
  • Ting Kang
  • Yulin Li
  • Jiaming Liang
  • Xiaoqi Sheng

With the advancement of deep learning, polyp segmentation in endoscopic images has achieved remarkable progress. However, clinical polyps often exhibit variable morphology, blurred boundaries, and low contrast with the intestinal mucosa, hindering accurate lesion localization and edge delineation. Moreover, complex conditions of low light, luminal distortion, and mucosal folds further exacerbate the problem with identification, resulting in frequent misdetections and omissions in computer-aided diagnosis. Accordingly, we propose Waveformer, a local-global co-modeling segmentation network, to improve segmentation accuracy. Concretely, the encoder employs parallel CNN-Transformer branches to synergistically extract detailed and global features, thereby enhancing the completeness and discriminative power of the representation. The decoder integrates a wavelet-based frequency decomposition unit (WFDU), a camouflage identification module (CIM), and an information fusion layer (IFL). These modules collaboratively enhance edge responses and semantic aggregation across scales, significantly boosting the framework's capability in boundary modeling and lesion discernment. Extensive experiments on CVC-ClinicDB and Kvasir-SEG datasets achieve Dice Similarity Coefficients (DSC) of 95. 60 % and 94. 11 %, outperforming fourteen state-of-the-art (SOTA) methods. Cross-dataset evaluations further verify its strong generalization ability, with DSC scores of 81. 0 % and 79. 2 %, respectively.

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IROS Conference 2025 Conference Paper

A Bio-inspired Spherical Soft Magnetic Millirobot for Gastrointestinal Applications

  • Yulin Li
  • Zhaorui Hong
  • Yuhao Zhao
  • Shuohao Zhang
  • Xin Zhao
  • Liu Yang

Gastroscopy and colonoscopy have become the fundamental tools for gastrointestinal (GI) tract diagnosis and treatment. Conventional tethered devices usually lead to the use of anesthetic agents and patient discomfort. Capsule endoscopy is becoming an ideal alternative, however, the smooth capsule shape may hinder its active locomotion and retention in the GI tract. Here, we propose a spherical soft magnetic millirobot (S 2 M 2 robot) that integrates a virus-like spherical body with protrusions and an octopus-inspired sucker design for enhanced physical capabilities. The protruding suckers (radius: 2. 2-4. 8 mm) on its surface enable efficient locomotion performance (maximum angular velocity: 8 r/s, maximum speed: 180 mm/s) with strong adhesive ability (maximum force: 3. 5 N). The ex vivo experiments in a swine stomach demonstrate the robot’s motion on the slippery surface of the gastric mucosa and the effectiveness of the pressure-based drug delivery system. The in vitro and ex vivo results highlight the superior mobility and controllability, showcasing its potential as a carrier robot for the next-generation capsule endoscopy.

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NeurIPS Conference 2025 Conference Paper

Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Prior

  • Yulin Li
  • Haokun GUI
  • Ziyang Fan
  • Junjie Wang
  • Bin Kang
  • Bin Chen
  • Zhuotao Tian

Recent advances in Video Large Language Models (VLLMs) have achieved remarkable video understanding capabilities, yet face critical efficiency bottlenecks due to quadratic computational growth with lengthy visual token sequences of long videos. While existing keyframe sampling methods can improve temporal modeling efficiency, additional computational cost is introduced before feature encoding, and the binary frame selection paradigm is found suboptimal. Therefore, in this work, we propose Dy namic To ken compression via LLM-guided K eyframe prior ( DyToK ), a training-free paradigm that enables dynamic token compression by harnessing VLLMs' inherent attention mechanisms. Our analysis reveals that VLLM attention layers naturally encoding query-conditioned keyframe priors, by which DyToK dynamically adjusts per-frame token retention ratios, prioritizing semantically rich frames while suppressing redundancies. Extensive experiments demonstrate that DyToK achieves state-of-the-art efficiency-accuracy tradeoffs. DyToK shows plug-and-play compatibility with existing compression methods, such as VisionZip and FastV, attaining 2. 5x faster inference while preserving accuracy across multiple VLLMs, such as LLaVA-OneVision and Qwen2. 5-VL. Code and models will be made publicly available.

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AAAI Conference 2025 Conference Paper

OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision

  • Junjie Wang
  • Bin Chen
  • Bin Kang
  • Yulin Li
  • Weizhi Xian
  • Yichi Chen
  • Yong Xu

Open-vocabulary detection aims to detect objects from novel categories beyond the base categories on which the detector is trained. However, existing open-vocabulary detectors trained on base category data tend to assign higher confidence to trained categories and confuse novel categories with the background. To resolve this, we propose OV-DQUO, an Open-Vocabulary DETR with Denoising text Query training and open-world Unknown Objects supervision. Specifically, we introduce a wildcard matching method. This method enables the detector to learn from pairs of unknown objects recognized by the open-world detector and text embeddings with general semantics, mitigating the confidence bias between base and novel categories. Additionally, we propose a denoising text query training strategy. It synthesizes foreground and background query-box pairs from open-world unknown objects to train the detector through contrastive learning, enhancing its ability to distinguish novel objects from the background. We conducted extensive experiments on the OV-COCO and OV-LVIS benchmarks, achieving new state-of-the-art results of 45.6 AP50 and 39.3 mAP on novel categories, respectively.

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IJCAI Conference 2023 Conference Paper

Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding

  • Mingliang Zhai
  • Yulin Li
  • Xiameng Qin
  • Chen Yi
  • Qunyi Xie
  • Chengquan Zhang
  • Kun Yao
  • Yuwei Wu

Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the sequence length. General efficient transformers are challenging to be directly adapted to model document. They are unable to handle the layout representation in documents, e. g. word, line and paragraph, on different granularity levels and seem hard to achieve a good trade-off between efficiency and performance. To tackle the concerns, we propose Fast-StrucTexT, an efficient multi-modal framework based on the StrucTexT algorithm with an hourglass transformer architecture, for visual document understanding. Specifically, we design a modality-guided dynamic token merging block to make the model learn multi-granularity representation and prunes redundant tokens. Additionally, we present a multi-modal interaction module called Symmetry Cross-Attention (SCA) to consider multi-modal fusion and efficiently guide the token mergence. The SCA allows one modality input as query to calculate cross attention with another modality in a dual phase. Extensive experiments on FUNSD, SROIE, and CORD datasets demonstrate that our model achieves the state-of-the-art performance and almost 1. 9x faster inference time than the state-of-the-art methods.

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ICLR Conference 2023 Conference Paper

StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training

  • Yuechen Yu
  • Yulin Li
  • Chengquan Zhang
  • Xiaoqiang Zhang 0006
  • Zengyuan Guo
  • Xiameng Qin
  • Kun Yao
  • Junyu Han

In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling, based on text region-level image masking. The proposed method randomly masks some image regions according to the bounding box coordinates of text words. The objectives of our pre-training tasks are reconstructing the pixels of masked image regions and the corresponding masked tokens simultaneously. Hence the pre-trained encoder can capture more textual semantics in comparison to the masked image modeling that usually predicts the masked image patches. Compared to the masked multi-modal modeling methods for document image understanding that rely on both the image and text modalities, StrucTexTv2 models image-only input and potentially deals with more application scenarios free from OCR pre-processing. Extensive experiments on mainstream benchmarks of document image understanding demonstrate the effectiveness of StrucTexTv2. It achieves competitive or even new state-of-the-art performance in various downstream tasks such as image classification, layout analysis, table structure recognition, document OCR, and information extraction under the end-to-end scenario.

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v2026.09.13