Face detection is the basic step in video face analysis and has been studied for many years. However, achieving real-time performance on computation-resource-limited embedded devices still remains an open challenge. To address this problem, in this paper we propose a face detector, EagleEye, which shows a good trade-off between high accuracy and fast speed on the popular embedded device with low computation power (e.g., the Raspberry Pi 3b+). The EagleEye is designed to have low floating-point operations per second (FLOPS) as well as enough capacity, and its accuracy is further improved without adding too much FLOPS. Specifically, we design five strategies for building efficient face detectors with a good balance of accuracy and running speed. The first two strategies help to build a detector with low computation complexity and enough capacity. We use convolution factorization to change traditional convolutions into more sparse depth-wise convolutions to save computation costs and we use successive downsampling convolutions at the beginning of the face detection network. The latter three strategies significantly improve the accuracy of the light-weight detector without adding too much computation costs. We design an efficient context module to utilize context information to benefit the face detection. We also adopt information preserving activation function to increase the network capacity. Finally, we use focal loss to further improve the accuracy by handling the class imbalance problem better. Experiments show that the EagleEye outperforms the other face detectors with the same order of computation costs, on both runtime efficiency and accuracy.

Multi-modal fusion methods combine the advantages of both point clouds and RGB images to boost the performance of 3D object detection. Despite the significant progress, we find that existing two-stage multi-modal fusion methods suffer from the 3D proposal missing in the first stage and projected-style feature fusion mechanism. To solve these problems, we propose a two-stage multi-modal feature fusion network, which improves the recall rate of hard targets in the first stage of network with pseudo 3D proposals generated from image candidates. Then, considering the complementary information between similar image foreground features across multiple objects, we design a multi-modal cross-target fusion module to pay more attention to the foreground objects. It enables a 3D proposal can aggregate the semantic features of multiple image candidates belonging to the same category. Finally, these enhanced fused proposals are processed in the second stage to further boost the performance of 3D detector. Experimental results on SUN RGB-D and KITTI datasets show the effectiveness of our proposed method.

Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.

Mask face recognition has recently gained increasing attention in the current context. Face mask occlusion seriously affects the performance of face recognition systems, because more than 75% of the face area remains unexposed and the mask directly causes an increase in intra-class differences and a decrease in inter-class separability in the feature space. To improve the performance of face recognition model against mask occlusion, we propose a fast and efficient method for mask generation in this paper, which can avoid the need for large-scale collection of real-world mask face training sets. This approach can be embedded in the training process of any mask face model as a module and is very flexible. Experiments on the MLFW, MFR2 and RMFD datasets show the effectiveness and flexibility of our approach that outperform the state-of-the-art methods.