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Jiahui Chen

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

CroPS: Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search

Ao Xie
Jiahui Chen
Quanzhi Zhu
Xiaoze Jiang
Zhiheng Qin
Enyun Yu
Han Li

Dense retrieval has become a foundational paradigm in modern search systems, especially on short-video platforms. However, most industrial systems adopt a self-reinforcing training pipeline that relies on historically exposed user interactions for supervision. This paradigm inevitably leads to a filter bubble effect, where potentially relevant but previously unseen content is excluded from the training signal, biasing the model toward narrow and conservative retrieval. In this paper, we present CroPS (Cross-Perspective Positive Samples), a novel retrieval data engine designed to alleviate this problem by introducing diverse and semantically meaningful positive examples from multiple perspectives. CroPS enhances training with positive signals derived from user query reformulation behavior (query-level), engagement data in recommendation streams (system-level), and world knowledge synthesized by large language models (knowledge-level). To effectively utilize these heterogeneous signals, we introduce a Hierarchical Label Assignment (HLA) strategy and a corresponding H-InfoNCE loss that together enable fine-grained, relevance-aware optimization. Extensive experiments conducted on Kuaishou Search, a large-scale commercial short-video search platform, demonstrate that CroPS significantly outperforms strong baselines both offline and in live A/B tests, achieving superior retrieval performance and reducing query reformulation rates. CroPS is now fully deployed in Kuaishou Search, serving hundreds of millions of users daily.

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IROS Conference 2025 Conference Paper

3D Vision-tactile Reconstruction from Infrared and Visible Images for Robotic Fine-grained Tactile Perception

Yuankai Lin
Xiaofan Lu
Jiahui Chen
Hua Yang

To achieve human-like haptic perception in anthropomorphic grippers, the compliant sensing surfaces of vision tactile sensor (VTS) must evolve from conventional planar configurations to biomimetically curved topographies with continuous surface gradients. However, planar VTSs have challenges when extended to curved surfaces, including insufficient lighting of surfaces, blurring in reconstruction, and complex spatial boundary conditions for surface structures. With an end goal of constructing a human-like fingertip, our research (i) develops GelSplitter3D by expanding imaging channels with a prism and a near-infrared (NIR) camera, (ii) proposes a photometric stereo neural network with a CAD-based normal ground truth generation method to calibrate tactile geometry, and (iii) devises a normal integration method with boundary constraints of depth prior information to correcting the cumulative error of surface integrals. We demonstrate better tactile sensing performance, a 40% improvement in normal estimation accuracy, and the benefits of sensor shapes in grasping and manipulation tasks.

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EAAI Journal 2025 Journal Article

Influence factor-based transformation method for translating mass function to probability in Dempster–Shafer evidence theory

Haocheng Shao
Lipeng Pan
Jiahui Chen
Xiaozhuan Gao
Bingyi Kang

Dempster–Shafer evidence theory provides an effective mathematical tool to represent uncertain information by assigning information into power set. Among its associated studies, a pivotal challenge is the transformation of mass functions into probability distributions which can enhance the robustness and reliability of decision-making. In this paper, influence factor is constructed by considering the impact of transformation between multi-element propositions and single-element propositions. Then based on influence factor, the novel transformation method is proposed. In addition, some numerical examples are used to explain effectiveness of new method by analyzing the probability information capacity of different methods. Finally, this paper applies the novel method to target recognition and validates its effectiveness as well as its enhanced support for decision-making through the utilization of real-world datasets.

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IJCAI Conference 2024 Conference Paper

D3ETR: Decoder Distillation for Detection Transformer

Xiaokang Chen
Jiahui Chen
Yan Liu
Jiaxiang Tang
Gang Zeng

Although various knowledge distillation (KD) methods for CNN-based detectors have been proven effective in improving small students, build- ing baselines and recipes for DETR-based detec- tors remains a challenge. This paper concentrates on the transformer decoder of DETR-based detec- tors and explores KD methods suitable for them. However, the random order of the decoder outputs poses a challenge for knowledge distillation as it provides no direct correspondence between the pre- dictions of the teacher and the student. To this end, we propose MixMatcher that aligns the de- coder outputs of DETR-based teacher and student, by mixing two teacher-student matching strategies for combined advantages. The first strategy, Adap- tive Matching, applies bipartite matching to adap- tively match the outputs of the teacher and the stu- dent in each decoder layer. The second strategy, Fixed Matching, fixes the correspondence between the outputs of the teacher and the student with the same object queries as input, which alleviates in- stability of bipartite matching in Adaptive Match- ing. Using both strategies together produces bet- ter results than using either strategy alone. Based on MixMatcher, we devise Decoder Distillation for DEtection TRansformer (D3ETR), which dis- tills knowledge in decoder predictions and attention maps from the teacher to student. D3ETR shows superior performance on various DETR-based de- tectors with different backbones. For instance, D3ETR improves Conditional DETR-R50-C5 by 8. 3 mAP under 12 epochs training setting with Conditional DETR-R101-C5 serving as the teacher. The code will be released.

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TMLR Journal 2023 Journal Article

CAE v2: Context Autoencoder with CLIP Latent Alignment

Xinyu Zhang
Jiahui Chen
Junkun Yuan
Qiang Chen
Jian Wang
Xiaodi Wang
Shumin Han
Xiaokang Chen

Masked image modeling (MIM) learns visual representations by predicting the masked patches on a pre-defined target. Inspired by MVP(Wei et al., 2022b) that displays impressive gains with CLIP, in this work, we also employ the semantically rich CLIP latent as target and further tap its potential by introducing a new MIM pipeline, CAE v2, to learn a high-quality encoder and facilitate model convergence on the pre-training task. CAE v2 is an improved variant of CAE (Chen et al., 2023), applying the CLIP latent on two pretraining tasks, i.e., visible latent alignment and masked latent alignment. Visible latent alignment directly mimics the visible latent representations from the encoder to the corresponding CLIP latent, which is beneficial for facilitating model convergence and improving the representative ability of the encoder. Masked latent alignment predicts the representations of masked patches within the feature space of CLIP latent as standard MIM task does, effectively aligning the representations computed from the encoder and the regressor into the same domain. We pretrain CAE v2 on ImageNet-1K images and evaluate on various downstream vision tasks, including image classification, semantic segmentation, object detection and instance segmentation. Experiments show that our CAE v2 achieves competitive performance and even outperforms the CLIP vision encoder, demonstrating the effectiveness of our method. Code is available at https://github.com/Atten4Vis/CAE.

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EAAI Journal 2022 Journal Article

On-shelf utility mining from transaction database

Jiahui Chen
Xu Guo
Wensheng Gan
Chien-Ming Chen
Weiping Ding
Guoting Chen

As an important technique for dealing with transaction database in the field of data mining, utility-driven mining can be used to discover useful patterns (i. e. , itemsets, sequences) which have a high utility. However, it has a bias towards the item/object combinations which have more exhibition period since they have more opportunity to generate a high utility. To address this, the on-shelf time period of items need to be considered, thus on-shelf utility mining (OSUM) can be applied in the application which is more closer to the actual situation. Currently several models have been proposed to deal with the OSUM problem, but they still suffer from the requirement that it needs to maintain a massive candidates in memory and to scan database many times. In this paper, we propose two effective one-phase algorithms named OSUMI (On-Shelf Utility Mining from transactIon database) and OSUMI + (the improve version of OSUMI). Both OSUMI and OSUMI + search all itemsets as a set-enumeration tree and discover the on-shelf itemsets with high utility in a more practical way. More precisely, in order to avoid the problems of high memory consumption, two algorithms apply some properties of the concept of on-shelf utility. Besides, two upper-bounds named subtree utility and local utility are applied to early filter out unpromising patterns and then prune the search space. Finally, an extensive experimental study on several real on-shelf datasets shows that our proposed algorithms can be significantly faster than the state-of-the-art algorithm.

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AAAI Conference 2021 Conference Paper

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

Chao Huang
Jiahui Chen
Lianghao Xia
Yong Xu
Peng Dai
Yanqing Chen
Liefeng Bo
Jiashu Zhao

Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existing session-based recommendation techniques (e. g. , attentionbased recurrent network or graph neural network) are not well-designed for capturing the complex transition dynamics exhibited with temporally-ordered and multi-level interdependent relation structures. These methods largely overlook the relation hierarchy of item transitional patterns. In this paper, we propose a multi-task learning framework with Multi-level Transition Dynamics (MTD), which enables the jointly learning of intra- and inter-session item transition dynamics in automatic and hierarchical manner. Towards this end, we ﬁrst develop a position-aware attention mechanism to learn item transitional regularities within individual session. Then, a graph-structured hierarchical relation encoder is proposed to explicitly capture the cross-session item transitions in the form of high-order connectivities by performing embedding propagation with the global graph context. The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and highlevel item relationships in a common latent space. Extensive experiments on three real-world datasets demonstrate the superiority of MTD as compared to state-of-the-art baselines.

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TCS Journal 2020 Journal Article

A new encryption scheme for multivariate quadratic systems

Jiahui Chen
Jianting Ning
Jie Ling
Terry Shue Chien Lau
Yacheng Wang

It is regarded as a difficult task to design a secure MPKC fundamental schemes such as an encryption scheme. In this paper we introduce a new central trapdoor for multivariate quadratic (MQ) public-key cryptosystems that allows for encryption, in contrast to time-tested MQ primitives such as Unbalanced Oil and Vinegar or Rainbow which only allow for signatures. The same as UOV or Rainbow, our construction is single field scheme where the central polynomial system is chosen to have a particular structure that enables efficient inversion. After applying this transformation, the plaintext can be recovered by solving a linear system. Our new central trapdoor can use to replace the broken extension field calculation trapdoor and simple matrix encryption trapdoor, thereafter, we use the minus and plus modifiers to inoculate our scheme against known attacks. It is highlight that our encryption scheme is a good explore in the area of multivariate cryptography. Finally, a straightforward Magma implementation confirms the efficient operation of the public key algorithms.

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ECAI Conference 2020 Conference Paper

Joint Modeling of Local and Global Behavior Dynamics for Session-Based Recommendation

Yong Xu 0007
Jiahui Chen
Chao Huang 0001
Bo Zhang
Hao Xing
Peng Dai 0001
Liefeng Bo

Session-based recommendation is critical in modern recommender systems, which aims to predict the next interested item given anonymous behavior sequences of users. While prior works have made efforts to addressing the session-based recommendation problem, two significant limitations exist: i) They ignore the fact that items may be correlated with other across different session units; ii) existing solutions are also limited in their assumption of rigidly ordered pattern over intra-session item transition, which may not be true in practice. To address these above limitations, we propose a Local-Global Session-based Recommendation framework–LGSR which generalizes the modeling of behavior dynamics from two perspectives: we first design a cross-session item dependency encoder to learn the inter-session item relation structures from a global perspective. Additionally, a dual-stage attentive aggregation module is developed to capture local item transition dynamics, without the restriction of rigid sequential process for jointly modeling user’s current interest and intra-session purpose. With the exploration of both complex intra- and inter-session interest transitional regularities, our LGSR model enables the representation learning of user behavior dynamics via jointly mapping local and global signals into the same latent space. The experimental results on two real-world datasets demonstrate the superiority of the proposed LGSR framework over state-of-the-art methods.

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