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Found 10 papers, 1 papers with code
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification
Deep learning technologies have already demonstrated a high potential to build diagnosis support systems from medical imaging data, such as Chest X-Ray images.
Graph Convolutional Network for Multi-Target Multi-Camera Vehicle Tracking
This letter focuses on the task of Multi-Target Multi-Camera vehicle tracking.
Attention-based Knowledge Distillation in Multi-attention Tasks: The Impact of a DCT-driven Loss
Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activation maps, as the source knowledge.
A study on the distribution of social biases in self-supervised learning visual models
We show that there is a correlation between the type of the SSL model and the number of biases that it incorporates.
Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs
Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems.
A Prospective Study on Sequence-Driven Temporal Sampling and Ego-Motion Compensation for Action Recognition in the EPIC-Kitchens Dataset
Action recognition is currently one of the top-challenging research fields in computer vision.
Egocentric Human Segmentation for Mixed Reality
The objective of this work is to segment human body parts from egocentric video using semantic segmentation networks.
Semantic-Aware Scene Recognition
Scene recognition is currently one of the top-challenging research fields in computer vision.
Ranked #1 on
Scene Recognition
on ADE20K
Semantic Driven Multi-Camera Pedestrian Detection
Contrarily to the majority of the methods of the state-of-the-art, the proposed approach is scene-agnostic, not requiring a tailored adaptation to the target scenario\textemdash e. g., via fine-tunning.
