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Found 8 papers, 3 papers with code
Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models
This approach limits the generation of segmentation masks derived from word tokens not contained in the text prompt.
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.
Soft labelling for semantic segmentation: Bringing coherence to label down-sampling
To that aim, we propose a novel framework for label down-sampling via soft-labeling that better conserves label information after down-sampling.
Detection-aware multi-object tracking evaluation
How would you fairly evaluate two multi-object tracking algorithms (i. e. trackers), each one employing a different object detector?
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.
Graph Neural Networks for Cross-Camera Data Association
To avoid the usage of fixed distances, we leverage the connectivity of Graph Neural Networks, previously unused in this scope, using a Message Passing Network to jointly learn features and similarity.
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.
On guiding video object segmentation
This guiding process relies on foreground masks from independent algorithms (i. e. state-of-the-art algorithms) to implement an attention mechanism that incorporates the spatial location of foreground and background to compute their separated representations.
