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Yupeng Li

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

BLADE: A Behavior-Level Data Augmentation Framework with Dual Fusion Modeling for Multi-Behavior Sequential Recommendation

Yupeng Li
Mingyue Cheng
Yucong Luo
Yitong Zhou
Qingyang Mao
Shijin Wang

Multi-behavior sequential recommendation aims to capture users' dynamic interests by modeling diverse types of user interactions over time. Although several studies have explored this setting, the recommendation performance remains suboptimal, mainly due to two fundamental challenges: the heterogeneity of user behaviors and data sparsity. To address these challenges, we propose BLADE, a framework that enhances multi-behavior modeling while mitigating data sparsity. Specifically, to handle behavior heterogeneity, we introduce a dual item-behavior fusion architecture that incorporates behavior information at both the input and intermediate levels, enabling preference modeling from multiple perspectives. To mitigate data sparsity, we design three behavior-level data augmentation methods that operate directly on behavior sequences rather than core item sequences. These methods generate diverse augmented views while preserving the semantic consistency of item sequences. These augmented views further enhance representation learning and generalization via contrastive learning. Experiments on three real-world datasets demonstrate the effectiveness of our approach.

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AAAI Conference 2026 Conference Paper

Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System

Haorui He
Yupeng Li
Bin Benjamin Zhu
Dacheng Wen
Reynold Cheng
Francis C. M. Lau

State-of-the-art (SOTA) fact-checking systems combat misinformation by employing autonomous LLM-based agents to decompose complex claims into smaller sub-claims, verify each sub-claim individually, and aggregate the partial results to produce verdicts with justifications (explanations for the verdicts). The security of these systems is crucial, as compromised fact-checkers can amplify misinformation, but remains largely underexplored. To bridge this gap, this work introduces a novel threat model against such fact-checking systems and presents Fact2Fiction, the first poisoning attack framework targeting SOTA agentic fact-checking systems. Fact2Fiction employs LLMs to mimic the decomposition strategy and exploit system-generated justifications to craft tailored malicious evidences that compromise sub-claim verification. Extensive experiments demonstrate that Fact2Fiction achieves 8.9%-21.2% higher attack success rates than SOTA attacks across various poisoning budgets and exposes security weaknesses in existing fact-checking systems, highlighting the need for defensive countermeasures.

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ICRA Conference 2024 Conference Paper

EfficientDPS: Efficient and End-to-End Depth-aware Panoptic Segmentation

Shengkai Wu
Liangliang Ren
Linfeng Gao
Yupeng Li
Wen-Yu Liu 0003

Depth-aware panoptic segmentation (DPS) combines image segmentation and monocular depth estimation in a single model to achieve semantic and geometry perception simultaneously. DPS task has important applications in the robot area but the previous DPS models are too heavy to be applied. Thus, we propose EfficientDPS, an efficient, end-to-end, and unified model for DPS. In our method, query features extracted with convolution networks are used to represent things/stuff. In this way, different vision tasks such as classification, segmentation, and depth estimation can be realized in a unified manner, leading to a compact and efficient model. EfficientDPS can be trained and tested in an end-to-end manner via bipartite matching and complex post-process is not needed at inference. To enhance the supervision signal, group query representation is proposed, leading to better performance without affecting the inference speed. Extensive experiments on Cityscapes-DPS and SemKITTI-DPS show that EfficientDPS can achieve the best trade-off between speed and accuracy than the state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

A Deep Bi-directional Attention Network for Human Motion Recovery

Qiongjie Cui
Huaijiang Sun
Yupeng Li
Yue Kong

Human motion capture (mocap) data, recording the movement of markers attached to specific joints, has gradually become the most popular solution of animation production. However, the raw motion data are often corrupted due to joint occlusion, marker shedding and the lack of equipment precision, which severely limits the performance in real-world applications. Since human motion is essentially a sequential data, the latest methods resort to variants of long short-time memory network (LSTM) to solve related problems, but most of them tend to obtain visually unreasonable results. This is mainly because these methods hardly capture long-term dependencies and cannot explicitly utilize relevant context, especially in long sequences. To address these issues, we propose a deep bi-directional attention network (BAN) which can not only capture the long-term dependencies but also adaptively extract relevant information at each time step. Moreover, the proposed model, embedded attention mechanism in the bi-directional LSTM (BLSTM) structure at the encoding and decoding stages, can decide where to borrow information and use it to recover corrupted frame effectively. Extensive experiments on CMU database demonstrate that the proposed model consistently outperforms other state-of-the-art methods in terms of recovery accuracy and visualization.

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