  Multi-modal reasoning plays a vital role in bridging the gap between textual
and visual information, enabling a deeper understanding of the context. This
paper presents the Feature Swapping Multi-modal Reasoning (FSMR) model,
designed to enhance multi-modal reasoning through feature swapping. FSMR
leverages a pre-trained visual-language model as an encoder, accommodating both
text and image inputs for effective feature representation from both
modalities. It introduces a unique feature swapping module, enabling the
exchange of features between identified objects in images and corresponding
vocabulary words in text, thereby enhancing the model's comprehension of the
interplay between images and text. To further bolster its multi-modal alignment
capabilities, FSMR incorporates a multi-modal cross-attention mechanism,
facilitating the joint modeling of textual and visual information. During
training, we employ image-text matching and cross-entropy losses to ensure
semantic consistency between visual and language elements. Extensive
experiments on the PMR dataset demonstrate FSMR's superiority over
state-of-the-art baseline models across various performance metrics.
