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Junbo Wang

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

NP-MiSR: Neural Process-based Multi-Interest Learning for Session-Based Recommendation

Jun Bao
Junbo Wang
Yiheng Jiang
Xiangfeng Liu
Mingyang Lv
Yuanbo Xu

Session-based recommendation (SBR) aims to provide users with satisfactory suggestions via modeling preferences based on short-term, anonymous user-item interaction sequences. Traditional single interest learning methods struggle to align with the diverse nature of preferences. Recent advances resolved this bottleneck by learning multiple interest embeddings for each session. However, due to the pre-defining scheme of interest quantity (e.g. the number of interests), these approaches are deficient in adaptive ability towards distinctive preference patterns across different users. Moreover, these methods rely solely on the current session and ignore useful information from related ones. The short-term property of sessions would magnify the insufficient representation issue. To address these limitations, we propose a Neural Process-based Multi-interest learning framework for Session-based Recommendation, namely NP-MiSR. To be specific, our method enables adaptive multi-interest representation learning through two complementary mechanisms: 1) Neural Process-based Intra-session interest modeling: We employ Neural Processes to model the distribution of interests within a session, where the fixed interest configurations are no longer needed. 2) Cross-session context fusion: We extract interest distributions of similar sessions as contextual priors to refine the current session’s interest representation. Extensive experiments on three datasets demonstrate that our method consistently outperforms state-of-the-art SBR approaches with an average improvement of 38.8%. Moreover, the few-shot learning task reveals that NP-MiSR achieves a surprisingly favorable efficiency v.s. performance trade-off where utilizing only 10% of the training data attains 95% of the recommendation performance.

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AAAI Conference 2026 Conference Paper

ReAlign: Text-to-Motion Generation via Step-Aware Reward-Guided Alignment

Wanjiang Weng
Xiaofeng Tan
Junbo Wang
Guo-Sen Xie
Pan Zhou
Hongsong Wang

Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based methods have been shown to generate more diversity and realistic motion. However, there exists a misalignment between text and motion distributions in diffusion models, which leads to semantically inconsistent or low-quality motions. To address this limitation, we propose Reward-guided sampling Alignment (ReAlign), comprising a step-aware reward model to assess alignment quality during the denoising sampling and a reward-guided strategy that directs the diffusion process toward an optimally aligned distribution. This reward model integrates step-aware tokens and combines a text-aligned module for semantic consistency and a motion-aligned module for realism, refining noisy motions at each timestep to balance probability density and alignment. Extensive experiments of both motion generation and retrieval tasks demonstrate that our approach significantly improves text-motion alignment and motion quality compared to existing state-of-the-art methods.

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EAAI Journal 2025 Journal Article

Contrastive deep graph clustering via higher-order heuristic augmentation and propagation

Zheyu Zheng
Jianrui Chen
Junjie Huang
Junbo Wang

Deep graph clustering is a critical technique for applications such as social network analysis, bioinformatics, and recommender systems, aimed at dividing nodes in a graph into disjoint clusters by leveraging the learning capabilities of deep neural networks. Current clustering methods face issues like poor alignment with tasks, reliance on fixed augmentations (causing semantic drift and info loss), and real-world data sparsity. These methods also neglect higher-order structure in sparse data, hampering their ability to capture node relations. To address these issues, we propose a Contrastive deep Graph Clustering method via Higher-order heuristic Augmentation and Propagation (CGC-HAP). Our higher-order heuristic augmentation strategy enriches intra-cluster connections by uncovering hidden structural and semantic information from local to global perspectives. Additionally, our higher-order information propagation ensures the dissemination of detailed information throughout the graph. Extensive experiments on five benchmark datasets demonstrate that CGC-HAP significantly outperforms state-of-the-art methods in clustering accuracy and robustness. Our implementations will be publicly available at: https: //github. com/jianruichen/CGC-HAP.

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AAAI Conference 2025 Conference Paper

USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation

Wanjiang Weng
Hongsong Wang
Junbo Wang
Lei He
Guo-Sen Xie

Contrastive learning has achieved great success in skeleton-based representation learning recently. However, the prevailing methods are predominantly negative-based, necessitating additional momentum encoder and memory bank to get negative samples, which increases the difficulty of model training. Furthermore, these methods primarily concentrate on learning a global representation for recognition and retrieval tasks, while overlooking the rich and detailed local representations that are crucial for dense prediction tasks. To alleviate these issues, we introduce a Unified Skeleton-based Dense Representation Learning framework based on feature decorrelation, called USDRL, which employs feature decorrelation across temporal, spatial, and instance domains in a multi-grained manner to reduce redundancy among dimensions of the representations to maximize information extraction from features. Additionally, we design a Dense Spatio-Temporal Encoder (DSTE) to capture fine-grained action representations effectively, thereby enhancing the performance of dense prediction tasks. Comprehensive experiments, conducted on the benchmarks NTU-60, NTU-120, PKU-MMD I, and PKU-MMD II, across diverse downstream tasks including action recognition, action retrieval, and action detection, conclusively demonstrate that our approach significantly outperforms the current state-of-the-art (SOTA) approaches.

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IROS Conference 2021 Conference Paper

Towards Autonomous Parking using Vision-only Sensors

Yi Yang 0009
Miaoxin Pan
Sitan Jiang
Jianhang Wang
Wei Wang
Junbo Wang
Meiling Wang 0002

Existing autonomous parking solutions usually require special signs, pre-built maps or accurate ranging sensors to achieve reliable perception of the parking environment, but these methods are difficult to popularize because they either require preconditions or are expensive for production cars. In this paper, we propose a vision-only autonomous parking solution based on only six cameras. Through the appropriate depth estimation algorithms, our method obtains the pixel level depth of the image, and constructs a dense point cloud, so as to realize the fine perception of the parking environment. An improved Radon transform based parking space detection method are applied for better parking space detection method. Our proposed method achieves processing speed of above 5 Hz on a intermediate level computing platform. Furthermore, we demonstrate the practicability of the proposed system in real-world parking lots.

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AAAI Conference 2020 Conference Paper

Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search

Ya Jing
Chenyang Si
Junbo Wang
Wei Wang
Liang Wang
Tieniu Tan

Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To further capture the phrase-related visual body part, a ﬁne-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 % in terms of the top-1 metric.

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