Parametric classification for generalized category discovery: A baseline study
Abstract Generalized Category Discovery (GCD) aims to discover novel categories in
unlabelled datasets using knowledge learned from labelled samples. Previous studies …
unlabelled datasets using knowledge learned from labelled samples. Previous studies …
Self-supervised visual representation learning with semantic grouping
In this paper, we tackle the problem of learning visual representations from unlabeled scene-
centric data. Existing works have demonstrated the potential of utilizing the underlying …
centric data. Existing works have demonstrated the potential of utilizing the underlying …
Image classification with small datasets: Overview and benchmark
Image classification with small datasets has been an active research area in the recent past.
However, as research in this scope is still in its infancy, two key ingredients are missing for …
However, as research in this scope is still in its infancy, two key ingredients are missing for …
Equivariance and invariance inductive bias for learning from insufficient data
We are interested in learning robust models from insufficient data, without the need for any
externally pre-trained checkpoints. First, compared to sufficient data, we show why …
externally pre-trained checkpoints. First, compared to sufficient data, we show why …
Ood-cv: A benchmark for robustness to out-of-distribution shifts of individual nuisances in natural images
Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One
reason is that existing robustness benchmarks are limited, as they either rely on synthetic …
reason is that existing robustness benchmarks are limited, as they either rely on synthetic …
Temporal context aggregation for video retrieval with contrastive learning
The current research focus on Content-Based Video Retrieval requires higher-level video
representation describing the long-range semantic dependencies of relevant incidents …
representation describing the long-range semantic dependencies of relevant incidents …
No data augmentation? alternative regularizations for effective training on small datasets
L Brigato, S Mougiakakou - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
Solving image classification tasks given small training datasets remains an open challenge
for modern computer vision. Aggressive data augmentation and generative models are …
for modern computer vision. Aggressive data augmentation and generative models are …
Genetic programming-based evolutionary deep learning for data-efficient image classification
Data-efficient image classification is a challenging task that aims to solve image
classification using small training data. Neural network-based deep learning methods are …
classification using small training data. Neural network-based deep learning methods are …
Ood-cv-v2: An extended benchmark for robustness to out-of-distribution shifts of individual nuisances in natural images
Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One
reason is that existing robustness benchmarks are limited, as they either rely on synthetic …
reason is that existing robustness benchmarks are limited, as they either rely on synthetic …
Vipriors 3: Visual inductive priors for data-efficient deep learning challenges
The third edition of the" VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning"
workshop featured four data-impaired challenges, focusing on addressing the limitations of …
workshop featured four data-impaired challenges, focusing on addressing the limitations of …