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Found 7 papers, 3 papers with code
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
Large language and vision-language models are rapidly being deployed in practice thanks to their impressive capabilities in instruction following, in-context learning, and so on.
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction.
Amortised Invariance Learning for Contrastive Self-Supervision
Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations.
Quality Diversity for Visual Pre-Training
Notably, the augmentations used in both supervised and self-supervised training lead to features with high invariance to spatial and appearance transformations.
HyperInvariances: Amortizing Invariance Learning
Providing invariances in a given learning task conveys a key inductive bias that can lead to sample-efficient learning and good generalisation, if correctly specified.
FRIDA -- Generative Feature Replay for Incremental Domain Adaptation
Inspired by the notion of generative feature replay, we propose a novel framework called Feature Replay based Incremental Domain Adaptation (FRIDA) which leverages a new incremental generative adversarial network (GAN) called domain-generic auxiliary classification GAN (DGAC-GAN) for producing domain-specific feature representations seamlessly.
Generative Adversarial Network
Unsupervised Domain Adaptation
A Novel Actor Dual-Critic Model for Remote Sensing Image Captioning
We deal with the problem of generating textual captions from optical remote sensing (RS) images using the notion of deep reinforcement learning.
