Abstract Knowledge Distillation (KD) has been validated as an effective model compression
technique for learning compact object detectors. Existing state-of-the-art KD methods for …

Target detection is a revolutionary advancement in computer vision that provides the ability
to identify specific targets in images for a wide variety of applications, including but not …

Deep neural networks have consistently represented the state of the art in most computer
vision problems. In these scenarios, larger and more complex models have demonstrated …

Balancing the trade-off between accuracy and speed for obtaining higher performance
without sacrificing the inference time is a challenging topic for object detection task …

The current most advanced CNN-based detection models are nearly not deployable on
mobile devices with limited arithmetic power due to problems such as too many redundant …

The superior performances of pre-trained foundation models in various visual tasks
underscore their potential to enhance the 2D models' open-vocabulary ability. Existing …

Knowledge distillation (KD) is an effective method for compressing models in object
detection tasks. Due to limited computational capability, unmanned aerial vehicle-based …

Response-based distillation, a key form of knowledge distillation (KD), has been central to
KD research. However, the sequential training pipelines inherent to object detection models …

With the growing demand for software-defined vehicles (SDVs), deep learning-based
perception models have become increasingly important in intelligent transportation systems …

Deep neural network (DNN) is extensively explored for LiDAR-based 3D object detection, a
crucial perception task in the field of autonomous driving. However, the presence of …