The behavior emerges and becomes more robust as the architecture scales up its number of parameters.

Wefind that while scaling training data ormodel size can boost many vision-language model capabilities, scaling offers little benefit for reasoning or relations. Surprisingly, we also discover today's best VLMs struggle on simple digit recognition and counting tasks, e.g. MNIST, which much simpler networks can solve.

Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.

Many important real-world scenarios involve data structured as graphs.

Through extensive experiments on prevalent image datasets, we demonstrate the superiority of HASSOD over existing methods, thereby advancing the state of the art in self-supervised object detection. Notably, we improve Mask AR from 20.2 to 22.5 on L VIS, and from 17.0 to 26.0 on SA-1B.

Earlier research highlighted DMs' vulnerability todatapoisoning attacks, butthese studies placed stricter requirements than conventional methods like'BadNets' inimage classification.