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A comprehensive survey on contrastive learning
Contrastive Learning is self-supervised representation learning by training a model to
differentiate between similar and dissimilar samples. It has been shown to be effective and …
differentiate between similar and dissimilar samples. It has been shown to be effective and …
Fake it till you make it: Learning transferable representations from synthetic imagenet clones
Recent image generation models such as Stable Diffusion have exhibited an impressive
ability to generate fairly realistic images starting from a simple text prompt. Could such …
ability to generate fairly realistic images starting from a simple text prompt. Could such …
Backbone is all your need: A simplified architecture for visual object tracking
Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive
biases has recently drawn extensive interest. However, existing tracking approaches rely on …
biases has recently drawn extensive interest. However, existing tracking approaches rely on …
Usb: A unified semi-supervised learning benchmark for classification
Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …
unlabeled data to augment limited labeled samples. However, currently, popular SSL …
Siamese image modeling for self-supervised vision representation learning
Self-supervised learning (SSL) has delivered superior performance on a variety of
downstream vision tasks. Two main-stream SSL frameworks have been proposed, ie …
downstream vision tasks. Two main-stream SSL frameworks have been proposed, ie …
A fractional gradient descent algorithm robust to the initial weights of multilayer perceptron
For multilayer perceptron (MLP), the initial weights will significantly influence its
performance. Based on the enhanced fractional derivative extend from convex optimization …
performance. Based on the enhanced fractional derivative extend from convex optimization …
Humanbench: Towards general human-centric perception with projector assisted pretraining
Human-centric perceptions include a variety of vision tasks, which have widespread
industrial applications, including surveillance, autonomous driving, and the metaverse. It is …
industrial applications, including surveillance, autonomous driving, and the metaverse. It is …
A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision
Foundation vision-language models are currently transforming computer vision, and are on
the rise in medical imaging fueled by their very promising generalization capabilities …
the rise in medical imaging fueled by their very promising generalization capabilities …
A new benchmark: On the utility of synthetic data with blender for bare supervised learning and downstream domain adaptation
Deep learning in computer vision has achieved great success with the price of large-scale
labeled training data. However, exhaustive data annotation is impracticable for each task of …
labeled training data. However, exhaustive data annotation is impracticable for each task of …
The tunnel effect: Building data representations in deep neural networks
Deep neural networks are widely known for their remarkable effectiveness across various
tasks, with the consensus that deeper networks implicitly learn more complex data …
tasks, with the consensus that deeper networks implicitly learn more complex data …