Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Sequential recommendation aims to predict users' next interaction with items based on their
past engagement sequence. Recently, the advent of Large Language Models (LLMs) has …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

We present DRESS a large vision language model (LVLM) that innovatively exploits Natural
Language feedback (NLF) from Large Language Models to enhance its alignment and …

From AlphaGo to ChatGPT, the field of AI has launched a series of remarkable
achievements in recent years. Analyzing, comparing, and summarizing these achievements …

We present a unified perspective on tackling various human-centric video tasks by learning
human motion representations from large-scale and heterogeneous data resources …

Most existing distillation methods ignore the flexible role of the temperature in the loss
function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In …

The computation necessary for training Transformer-based language models has
skyrocketed in recent years. This trend has motivated research on efficient training …

We present a novel unsupervised domain adaption method for person re-identification (reID)
that generalizes a model trained on a labeled source domain to an unlabeled target domain …