Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks
L Wang, KJ Yoon - IEEE transactions on pattern analysis and …, 2021 - ieeexplore.ieee.org
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 …
solving the most complex problem statements. However, these models are huge in size with …
Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks
Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …
contextual information extraction and decision making. Beyond modeling advances, the …
Monovit: Self-supervised monocular depth estimation with a vision transformer
Self-supervised monocular depth estimation is an attractive solution that does not require
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …
The temporal opportunist: Self-supervised multi-frame monocular depth
Self-supervised monocular depth estimation networks are trained to predict scene depth
using nearby frames as a supervision signal during training. However, for many …
using nearby frames as a supervision signal during training. However, for many …
Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance
Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene
information from single camera images, which is trainable on arbitrary image sequences …
information from single camera images, which is trainable on arbitrary image sequences …
On the uncertainty of self-supervised monocular depth estimation
Self-supervised paradigms for monocular depth estimation are very appealing since they do
not require ground truth annotations at all. Despite the astonishing results yielded by such …
not require ground truth annotations at all. Despite the astonishing results yielded by such …
Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation
Self-supervised monocular depth estimation has been widely studied, owing to its practical
importance and recent promising improvements. However, most works suffer from limited …
importance and recent promising improvements. However, most works suffer from limited …
NTIRE 2024 challenge on HR depth from images of specular and transparent surfaces
This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and
Transparent surfaces held in conjunction with the New Trends in Image Restoration and …
Transparent surfaces held in conjunction with the New Trends in Image Restoration and …
Exploiting pseudo labels in a self-supervised learning framework for improved monocular depth estimation
We present a novel self-distillation based self-supervised monocular depth estimation (SD-
SSMDE) learning framework. In the first step, our network is trained in a self-supervised …
SSMDE) learning framework. In the first step, our network is trained in a self-supervised …
Rm-depth: Unsupervised learning of recurrent monocular depth in dynamic scenes
TW Hui - Proceedings of the IEEE/CVF conference on …, 2022 - openaccess.thecvf.com
Unsupervised methods have showed promising results on monocular depth estimation.
However, the training data must be captured in scenes without moving objects. To push the …
However, the training data must be captured in scenes without moving objects. To push the …