  In recent times there has been a surge of multi-modal architectures based on
Large Language Models, which leverage the zero shot generation capabilities of
LLMs and project image embeddings into the text space and then use the
auto-regressive capacity to solve tasks such as VQA, captioning, and image
retrieval. We name these architectures as "bridge-architectures" as they
project from the image space to the text space. These models deviate from the
traditional recipe of training transformer based multi-modal models, which
involve using large-scale pre-training and complex multi-modal interactions
through co or cross attention. However, the capabilities of bridge
architectures have not been tested on complex visual reasoning tasks which
require fine grained analysis about the image. In this project, we investigate
the performance of these bridge-architectures on the NLVR2 dataset, and compare
it to state-of-the-art transformer based architectures. We first extend the
traditional bridge architectures for the NLVR2 dataset, by adding object level
features to faciliate fine-grained object reasoning. Our analysis shows that
adding object level features to bridge architectures does not help, and that
pre-training on multi-modal data is key for good performance on complex
reasoning tasks such as NLVR2. We also demonstrate some initial results on a
recently bridge-architecture, LLaVA, in the zero shot setting and analyze its
performance.
