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Song Chen

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3 papers

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EAAI Journal 2025 Journal Article

Fast shallow multi-subnet detector for real-time object detection

Yuan Li
Mengdie Song
Ke Hu
Song Chen
Yi Kang

Real-time object detection algorithms, underpinned by Deep Neural Networks (DNNs), are extensively applied in fields like autonomous driving and security surveillance. However, current algorithms face issues of low hardware resource utilization and high synchronization delays between network layers when deployed on DNN hardware accelerators, adversely affecting overall performance and efficiency. To address these issues, we have proposed an innovative single-stage object detection framework, the Shallow Multi-Subnet Detector (SMS-Det). SMS-Det adopts a multi-parallel-shallow-subnet architecture, which reduces inter-layer synchronization latency by decreasing network depth. Furthermore, it fully utilizes DNN hardware accelerators by executing convolution operations in parallel, preventing resource underutilization and maximizing throughput. The proposed network is comprised of multiple parallel shallow subnets, each of which processes feature maps of different scales. The Feature Fusion Layer (FFL) ensures seamless information exchange across subnets, significantly improving the detection of small and occluded objects. Finally, we introduce the multi-scale channel attention projections to enhance the feature mapping between the teacher model and the student model in the training process. Experimental results on the Microsoft Common Objects in Context (MS COCO) dataset demonstrate that our model achieves a state-of-the-art mean Average Precision (mAP) of 42. 6%, surpassing You Only Look Once Version 5 Small (YOLOv5-S 37. 4%) with only 19. 4 Giga Floating Point Operations (GFLOPs) and 11. 0 million parameters. Our model obtains 156 Frames Per Second (FPS), achieving a real-time inference acceleration of 51. 4% compared to YOLOv5-S (103 FPS).

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AAAI Conference 2024 Conference Paper

DALDet: Depth-Aware Learning Based Object Detection for Autonomous Driving

Ke Hu
Tongbo Cao
Yuan Li
Song Chen
Yi Kang

3D object detection achieves good detection performance in autonomous driving. However, it requires substantial computational resources, which prevents its practical application. 2D object detection has less computational burden but lacks spatial and geometric information embedded in depth. Therefore, we present DALDet, an efficient depth-aware learning based 2D detector, achieving high-performance object detection for autonomous driving. We design an efficient one-stage detection framework and seamlessly integrate depth cues into convolutional neural network by introducing depth-aware convolution and depth-aware average pooling, which effectively improve the detector's ability to perceive 3D space. Moreover, we propose a depth-guided loss function for training DALDet, which effectively improves the localization ability of the detector. Due to the use of depth map, DALDet can also output the distance of the object, which is of great importance for driving applications such as obstacle avoidance. Extensive experiments demonstrate the superiority and efficiency of DALDet. In particular, our DALDet ranks 1st on both KITTI Car and Cyclist 2D detection test leaderboards among all 2D detectors with high efficiency as well as yielding competitive performance among many leading 3D detectors. Code will be available at https://github.com/hukefy/DALDet.

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NeurIPS Conference 2023 Conference Paper

Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models

Yichao Cao
Qingfei Tang
Xiu Su
Song Chen
Shan You
Xiaobo Lu
Chang Xu

Human-object interaction (HOI) detection aims to comprehend the intricate relationships between humans and objects, predicting triplets, and serving as the foundation for numerous computer vision tasks. The complexity and diversity of human-object interactions in the real world, however, pose significant challenges for both annotation and recognition, particularly in recognizing interactions within an open world context. This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs). The proposed method is dubbed as UniHOI. We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning. Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image. Furthermore, we utilize a LLM (i. e. GPT) for interaction interpretation, generating a richer linguistic understanding for complex HOIs. For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence. Our efficient architecture design and learning methods effectively unleash the potential of the VL foundation models and LLMs, allowing UniHOI to surpass all existing methods with a substantial margin, under both supervised and zero-shot settings. The code and pre-trained weights will be made publicly available.

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