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Bowen Ma

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3 papers

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NeurIPS Conference 2025 Conference Paper

CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors

Mingyuan Li
Tong Jia
Hao Wang
Bowen Ma
Shiyi Guo
Da Cai
Dongyue Chen

Prohibited item detection based on X-ray images is one of the most effective security inspection methods. However, the foreground-background feature coupling caused by the overlapping phenomenon specific to X-ray images makes general detectors designed for natural images perform poorly. To address this issue, we propose a Category Semantic Prior Contrastive Learning (CSPCL) mechanism, which aligns the class prototypes perceived by the classifier with the content queries to correct and supplement the missing semantic information responsible for classification, thereby enhancing the model sensitivity to foreground features. To achieve this alignment, we design a specific contrastive loss, CSP loss, which comprises the Intra-Class Truncated Attraction (ITA) loss and the Inter-Class Adaptive Repulsion (IAR) loss, and outperforms classic contrastive losses. Specifically, the ITA loss leverages class prototypes to attract intra-class content queries and preserves essential intra-class diversity via a gradient truncation function. The IAR loss employs class prototypes to adaptively repel inter-class content queries, with the repulsion strength scaled by prototype-prototype similarity, thereby improving inter-class discriminability, especially among similar categories. CSPCL is general and can be easily integrated into Deformable DETR-based models. Extensive experiments on the PIXray, OPIXray, PIDray, and CLCXray datasets demonstrate that CSPCL significantly enhances the performance of various state-of-the-art models without increasing inference complexity. The code is publicly available at https: //github. com/Limingyuan001/CSPCL.

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EAAI Journal 2025 Journal Article

Detection of novel prohibited item categories for real-world security inspection

Shuyang Lin
Tong Jia
Hao Wang
Bowen Ma
Mingyuan Li
Dongyue Chen

The application of prohibited item detection in intelligent security inspection systems is essential for protecting public safety and categories of prohibited item are continuously increasing in accordance with the latest laws. Previous works all focus on closed-set scenarios, which can only recognize known categories used for training and often require time-consuming as well as labor-intensive annotations when learning novel categories, resulting in limited real-world applications. Although the success of vision-language models such us Contrastive Language-Image Pretraining (CLIP) provides a new perspective for X-ray prohibited item detection in open scenarios, directly applying CLIP to X-ray domain leads to a sharp performance drop due to domain shift between X-ray data and general data used for pre-training CLIP. To address aforementioned challenges, in this paper, we introduce distillation-based open-vocabulary object detection (OVOD) task into X-ray security inspection domain by extending CLIP to learn visual representations in specific X-ray domain, aiming to detect novel prohibited item categories beyond base categories on which the detector is trained. Specifically, we propose X-ray feature adapter and apply it to CLIP within OVOD framework to develop Open-Vocabulary X-ray prohibited item Detection (OVXD) model. Extensive experiments conducted on three mainstream X-ray security inspection datasets demonstrate that OVXD performs favorably against baseline methods in X-ray scenarios. Furthermore, OVXD can be directly transferred to different X-ray datasets without fine-tuning, verifying its generalization ability in real-world applications.

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AAAI Conference 2023 Conference Paper

FlowFace: Semantic Flow-Guided Shape-Aware Face Swapping

Hao Zeng
Wei Zhang
Changjie Fan
Tangjie Lv
Suzhen Wang
Zhimeng Zhang
Bowen Ma
Lincheng Li

In this work, we propose a semantic flow-guided two-stage framework for shape-aware face swapping, namely FlowFace. Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realistic face swapping. Concretely, our FlowFace consists of a face reshaping network and a face swapping network. The face reshaping network addresses the shape outline differences between the source and target faces. It first estimates a semantic flow (i.e. face shape differences) between the source and the target face, and then explicitly warps the target face shape with the estimated semantic flow. After reshaping, the face swapping network generates inner facial features that exhibit the identity of the source face. We employ a pre-trained face masked autoencoder (MAE) to extract facial features from both the source face and the target face. In contrast to previous methods that use identity embedding to preserve identity information, the features extracted by our encoder can better capture facial appearances and identity information. Then, we develop a cross-attention fusion module to adaptively fuse inner facial features from the source face with the target facial attributes, thus leading to better identity preservation. Extensive quantitative and qualitative experiments on in-the-wild faces demonstrate that our FlowFace outperforms the state-of-the-art significantly.

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