A multimodal benchmark and improved architecture for zero shot learning

In this work, we demonstrate that due to the inadequacies in the existing evaluation protocols and datasets, there is a need to revisit and comprehensively examine the multimodal Zero-Shot Learning (MZSL) problem formulation. Specifically, we address two major challenges faced by current MZSL approaches; (1) Established baselines are frequently incomparable and occasionally even flawed since existing evaluation datasets often have some overlap with the training dataset, thus violating the zero-shot paradigm; (2) Most existing methods are biased towards seen classes, which significantly reduces the performance when evaluated on both seen and unseen classes. To address these challenges, we first introduce a new multimodal dataset for zero-shot evaluation called MZSL-50 with 4462 videos from 50 widely diversified classes and no overlap with the training data. Further, we propose a novel multimodal zeroshot transformer (MZST) architecture that leverages attention bottlenecks for multimodal fusion. Our model directly predicts the semantic representation and is superior at reducing the bias towards seen classes. We conduct extensive ablation studies, and achieve state-of-the-art results on three benchmark datasets and our novel MZSL-50 dataset. Specifically, we improve the conventional MZSL performance by a margin of 2.1%, 9.81% and 8.68% on VGGSound, UCF-101 and ActivityNet, respectively. Finally, we expect the introduction of the MZSL-50 dataset will promote the future in-depth research on multimodal zero-shot learning in the community.