Transformers for real-time object detection
Transformers only look once with nonlinear combination for real-time object detection Xia, R., Li, G., Huang, Z., Pang, Y. and Qi, M. Year 2022 Neural Computing and Applications PublisherSpringer Nature ISSN 0941-0643 1433-3058 Xia, R., Li, G., Huang, Z., Pang, Y. and Qi, M. 2022. Transformers only look once with nonlinear combination for real-time object detection. Neural Computing and Applications. https://doi.org/10.1007/s00521-022-07333-y Abstract In this article, a novel real-time object detector called Transformers Only Look Once (TOLO) is proposed to resolve two problems. The first problem is the inefficiency of building long-distance dependencies among local features for amounts of modern real-time object detectors. The second one is the lack of inductive biases for vision Transformer networks with heavily computational cost. TOLO is composed of Convolutional Neural Network (CNN) backbone, Feature Fusion Neck (FFN), and different Lite Transformer Heads (LTHs), ...