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Found 13 papers, 4 papers with code
SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging
In this work, we present a modified CARLA simulator designed with LiDAR semantic segmentation in mind, with new classes, more consistent object labeling with their counterparts from real datasets such as SemanticKITTI, and the possibility to adjust the object class distribution.
Unsupervised Class Generation to Expand Semantic Segmentation Datasets
To mitigate this cost there has been a surge in the use of synthetically generated data -- usually created using simulators or videogames -- which, in combination with domain adaptation methods, can effectively learn how to segment real data.
Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances
This paper introduces a novel synthetic dataset that captures urban scenes under a variety of weather conditions, providing pixel-perfect, ground-truth-aligned images to facilitate effective feature alignment across domains.
Pinpoint Counterfactuals: Reducing social bias in foundation models via localized counterfactual generation
When applied to the Conceptual Captions dataset for creating gender counterfactuals, our method results in higher visual and semantic fidelity than state-of-the-art alternatives, while maintaining the performance of models trained using only real data on non-human-centric tasks.
Improved transferability of self-supervised learning models through batch normalization finetuning
At a cost of extra training of only 0. 16% model parameters, in case of ResNet-50, we acquire a proxy task that (i) has a stronger correlation with end-to-end finetuned performance, (ii) improves the linear probing performance in the many- and few-shot learning regimes and (iii) in some cases, outperforms both linear probing and end-to-end finetuning, reaching the state-of-the-art performance on a pathology dataset.
Ranked #1 on
Classification
on MHIST
(using extra training data)
SPIN: Spacecraft Imagery for Navigation
To address these limitations, we present SPIN (SPacecraft Imagery for Navigation), an open-source spacecraft image generation tool designed to support a wide range of visual navigation scenarios in space, with a particular focus on relative navigation tasks.
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.
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.
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.
Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach
State-of-the-art deep learning approaches for skin lesion recognition often require pretraining on larger and more varied datasets, to overcome the generalization limitations derived from the reduced size of the skin lesion imaging datasets.
FVV Live: A real-time free-viewpoint video system with consumer electronics hardware
The system has been designed to yield high-quality free-viewpoint video using consumer-grade cameras and hardware, which enables low deployment costs and easy installation for immersive event-broadcasting or videoconferencing.
FVV Live: Real-Time, Low-Cost, Free Viewpoint Video
FVV Live is a novel real-time, low-latency, end-to-end free viewpoint system including capture, transmission, synthesis on an edge server and visualization and control on a mobile terminal.
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.
