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Kai Tang

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

Dual Graph Disambiguation for Multi-Instance Partial-Label Learning

Zhen Zhu
Kai Tang
Songhe Feng
Yixuan Tang
Haobo Wang
Gengyu Lyu
Cheng Peng
Yining Sun

In multi-instance partial label learning (MIPL), each sample is a bag of multiple instances linked to a candidate label set containing one true and multiple false labels, yielding inexact supervision in both instance features and label space. However, existing works adopt decoupled approaches that focus exclusively on either instance-level feature fusion or label-level disambiguation, failing to fully exploit the intrinsic dependencies between these two spaces. To overcome this limitation, graph-based methods are widely recognized as a powerful paradigm in weakly supervised learning, yet their success hinges on reliable features—precisely what MIPL lacks due to instance-level noise. To bridge this gap, we propose DualG, a novel framework that simultaneously addresses feature learning and label disambiguation through dual-level graph propagation. Specifically, we construct dual relevance graphs at both the bag and instance levels. At the bag level, we build a similarity graph based on fused feature representations; at the instance level, we employ attention scores to filter out irrelevant instances and construct a reliable instance-level relevance graph. These complementary graphs enable our joint label disambiguation framework to simultaneously address inexact supervision signals in both instance space and label space. Experimental results on five benchmark datasets demonstrate that DualG outperforms existing MIPL and partial label learning methods, validating its effectiveness and superiority.

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AAAI Conference 2026 Conference Paper

Group-aware Multiscale Ensemble Learning for Test-Time Multimodal Sentiment Analysis

Kai Tang
Yixuan Tang
Tianyi Chen
Haokai Xu
Qiqi Luo
Jin Guang Zheng
Zhixin Zhang
Gang Chen

Multi-modal Sentiment Analysis (MSA) enables machines to perceive human sentiments by integrating multiple modalities such as text, video, and audio. Despite recent progress, most existing methods assume distribution consistency between training and test data—a condition rarely met in real-world scenarios. To address domain shifts without relying on source data or target labels, Test-Time Adaptation (TTA) has emerged as a promising paradigm. However, applying TTA methods to MSA faces two challenges: a representation bottleneck inherent to the regression formulation and the inconsistency in modality fusion caused by modality-specific data augmentation techniques. To overcome these issues, we propose Group-aware Multiscale Ensemble Learning (GMEL), which leverages a von Mises-Fisher (vMF) mixture distribution to model latent sentiment groups and integrates a multi-scale re-dropout strategy for modality-agnostic feature augmentation, preserving fusion consistency. Extensive experiments on three benchmark datasets using two backbone architectures show that GMEL significantly outperforms existing baselines, demonstrating strong robustness to test-time distribution shifts in multi-modal sentiment analysis.

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NeurIPS Conference 2025 Conference Paper

No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization

Wenhang Shi
Yiren Chen
Shuqing Bian
Xinyi Zhang
Kai Tang
Pengfei Hu
Zhe Zhao
Wei Lu

Prompt engineering is crucial for leveraging the full potential of large language models (LLMs). While automatic prompt optimization offers a scalable alternative to costly manual design, generating effective prompts remains challenging. Existing methods often struggle to stably generate improved prompts, leading to low efficiency, and overlook that prompt optimization easily gets trapped in local optima. Addressing this, we propose GRACE, a framework that integrates two synergistic strategies: Gated Refinement and Adaptive Compression, achieving Efficient prompt optimization. The gated refinement strategy introduces a feedback regulation gate and an update rejection gate, which refine update signals to produce stable and effective prompt improvements. When optimization stagnates, the adaptive compression strategy distills the prompt’s core concepts, restructuring the optimization trace and opening new paths. By strategically introducing information loss through refinement and compression, GRACE delivers substantial gains in performance and efficiency. In extensive experiments on 11 tasks across three practical domains, including BIG-Bench Hard (BBH), domain-specific, and general NLP tasks, GRACE achieves significant average relative performance improvements of 4. 7\%, 4. 4\% and 2. 7\% over state-of-the-art methods, respectively. Further analysis shows that GRACE achieves these gains using only 25\% of the prompt generation budget required by prior methods, highlighting its high optimization efficiency and low computational overhead. Our code is available at https: //github. com/Eric8932/GRACE.

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IROS Conference 2024 Conference Paper

Monocular Event-Inertial Odometry with Adaptive decay-based Time Surface and Polarity-aware Tracking

Kai Tang
Xiaolei Lang
Yukai Ma
Yuehao Huang
Laijian Li
Yong Liu 0007
Jiajun Lv

Event cameras have garnered considerable attention due to their advantages over traditional cameras in low power consumption, high dynamic range, and no motion blur. This paper proposes a monocular event-inertial odometry incorporating an adaptive decay kernel-based time surface with polarity-aware tracking. We utilize an adaptive decay-based Time Surface to extract texture information from asynchronous events, which adapts to the dynamic characteristics of the event stream and enhances the representation of environmental textures. However, polarity-weighted time surfaces suffer from event polarity shifts during changes in motion direction. To mitigate its adverse effects on feature tracking, we optimize the feature tracking by incorporating an additional polarityinverted time surface to enhance the robustness. Comparative analysis with visual-inertial and event-inertial odometry methods shows that our approach outperforms state-of-the-art techniques, with competitive results across various datasets.

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JBHI Journal 2022 Journal Article

AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy

Yunxiang Li
Guodong Zeng
Yifan Zhang
Jun Wang
Qun Jin
Lingling Sun
Qianni Zhang
Qisi Lian

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients’ treatment outcome. Nowadays, the evaluation is performed in a manual manner, which is time-consuming, subjective, and error-prone. In this article, we aim to automate this process by leveraging the advances in computer vision and artificial intelligence, to provide an objective and accurate method for root canal therapy result assessment. A novel anatomy-guided multi-branch Transformer (AGMB-Transformer) network is proposed, which first extracts a set of anatomy features and then uses them to guide a multi-branch Transformer network for evaluation. Specifically, we design a polynomial curve fitting segmentation strategy with the help of landmark detection to extract the anatomy features. Moreover, a branch fusion module and a multi-branch structure including our progressive Transformer and Group Multi-Head Self-Attention (GMHSA) are designed to focus on both global and local features for an accurate diagnosis. To facilitate the research, we have collected a large-scale root canal therapy evaluation dataset with 245 root canal therapy X-ray images, and the experiment results show that our AGMB-Transformer can improve the diagnosis accuracy from 57. 96% to 90. 20% compared with the baseline network. The proposed AGMB-Transformer can achieve a highly accurate evaluation of root canal therapy. To our best knowledge, our work is the first to perform automatic root canal therapy evaluation and has important clinical value to reduce the workload of endodontists.

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