Text-to-Query systems have surprisingly not been investigated so far.

In this paper, we study the same problem in the Massart noise model that we now define.

Deep reinforcement learning (RL) has achieved remarkable successes in complex tasks, e.g Go [

The resulting Hummingbird model, suitably prompted, performs various scene understanding tasks without modification while approaching the performance of specialists that have been finetuned for each task.

The concept of "incidental correlations" is derived from the notion of incidental endogeneity [

While recent research suggests that current large Vision-Language Models (VLMs) exhibit more reliance on shape, we find them to still be seriously limited in this regard.

While existing defenses often rely on regularization techniques to reduce information leakage, they remain vulnerable to recent attacks.

This leads to the proposal of a novel self-supervised solution based on a masked loss function. We evaluate our method using both simulated and real data. On simulated data from a conventional video, we achieve 34.35 mean PSNR with extremely photon-sparse binary input (<0.06 photons per pixel per frame).