Few-shot classification requires adapting knowledge learned from a large annotated base dataset to recognize novel unseen classes, each represented by few labeled examples. In such scenario, pretraining network with high capacity on the and then finetuning it examples causes severe overfitting. At same time, training simple linear classifier top of ``frozen'' features fails adapt model properti...

In this paper we propose a global convex approach for image hallucination. Altering the idea of classical multi image super resolution (SU) systems to single image SU, we incorporate aligned images to hallucinate the output. Our work is based on the paper of Tappen et al.[14] where they use a non-convex model for image hallucination. In comparison we formulate a convex primal optimization probl...

We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD). In contrast to existing studies that mostly ignore or assume pre-aligned face spatial configuration (e.g. facial landmarks localization or dense correspondence field), we alternatingly optimize two complementary tasks, namely face hallucination and dense corre...

Abstract The goal of information-seeking dialogue is to respond seeker queries with natural language utterances that are grounded on knowledge sources. However, systems often produce unsupported utterances, a phenomenon known as hallucination. To mitigate this behavior, we adopt data-centric solution and create FaithDial, new benchmark for hallucination-free dialogues, by editing hallucinated r...