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Found 7 papers, 0 papers with code
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation
Humans use UAVs to monitor changes in forest environments since they are lightweight and provide a large variety of surveillance data.
MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation
Depth-aware video panoptic segmentation tackles the inverse projection problem of restoring panoptic 3D point clouds from video sequences, where the 3D points are augmented with semantic classes and temporally consistent instance identifiers.
Time-Space Transformers for Video Panoptic Segmentation
We propose a novel solution for the task of video panoptic segmentation, that simultaneously predicts pixel-level semantic and instance segmentation and generates clip-level instance tracks.
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation
To improve the performance of our estimates, in the second step, we re-train the network with the scale invariant logarithmic loss supervised by pseudo labels.
Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features
In this paper we propose a novel boosting-based sliding window solution for object detection which can keep up with the precision of the state-of-the art deep learning approaches, while being 10 to 100 times faster.
Semantic Channels for Fast Pedestrian Detection
However most of the top performing approaches provide state of art results at high computational costs.
Word Channel Based Multiscale Pedestrian Detection Without Image Resizing and Using Only One Classifier
By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.
