TraVLR: Now You See It, Now You Don't! Evaluating Cross-Modal Transfer of Visio-Linguistic Reasoning

Numerous visio-linguistic (V+L) representation learning methods have been developed, yet existing datasets do not evaluate the extent to which they represent visual and linguistic concepts in a unified space. Inspired by the crosslingual transfer and psycholinguistics literature, we propose a novel evaluation setting for V+L models: zero-shot cross-modal transfer. Existing V+L benchmarks also often report global accuracy scores on the entire dataset, rendering it difficult to pinpoint the specific reasoning tasks that models fail and succeed at. To address this issue and enable the evaluation of cross-modal transfer, we present TraVLR, a synthetic dataset comprising four V+L reasoning tasks. Each example encodes the scene bimodally such that either modality can be dropped during training/testing with no loss of relevant information. TraVLR's training and testing distributions are also constrained along task-relevant dimensions, enabling the evaluation of out-of-distribution generalisation. We evaluate four state-of-the-art V+L models and find that although they perform well on the test set from the same modality, all models fail to transfer cross-modally and have limited success accommodating the addition or deletion of one modality. In alignment with prior work, we also find these models to require large amounts of data to learn simple spatial relationships. We release TraVLR as an open challenge for the research community.