Abstract Knowledge distillation transfers knowledge from the teacher network to the student
one, with the goal of greatly improving the performance of the student network. Previous …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

State-of-the-art distillation methods are mainly based on distilling deep features from
intermediate layers, while the significance of logit distillation is greatly overlooked. To …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

In this work, we present a unified framework for multi-modality 3D object detection, named
UVTR. The proposed method aims to unify multi-modality representations in the voxel space …

Abstract Knowledge distillation has been applied to image classification successfully.
However, object detection is much more sophisticated and most knowledge distillation …

Unlike existing knowledge distillation methods focus on the baseline settings, where the
teacher models and training strategies are not that strong and competing as state-of-the-art …

Abstract Current Knowledge Distillation (KD) methods for semantic segmentation often
guide the student to mimic the teacher's structured information generated from individual …

Abstract Knowledge distillation has been applied to various tasks successfully. The current
distillation algorithm usually improves students' performance by imitating the output of the …

We present Neural Feature Fusion Fields (N3F),\a method that improves dense 2D image
feature extractors when the latter are applied to the analysis of multiple images …