Capture Concept Through Comparison: Vision-and-Language Representation Learning with Intrinsic Information Mining

Achieving alignment between vision and language semantics poses a critical challenge. Prior works have sought to enhance alignment by incorporating additional supervision, such as tags or object bounding boxes, as anchors between modalities. However, these methods predominantly concentrate on aligning tangible entities, disregarding other crucial abstract concepts that elude perception, such as side by side. To overcome this limitation, we propose a novel approach to Capture various Concepts through data Comparison (C3) for learning cross-modal representations. Specifically, we devise a data mining procedure to uncover intrinsic information within the database, avoiding the need for external annotations. Furthermore, we distinctly frame model inputs as triplets to better elucidate abstract semantics in images. Building upon this formulation, we propose two concept-centric pre-training objectives to signify concept learning. Extensive experiments show that models trained within the C3 framework consistently achieve significant enhancements across a wide range of comprehension and reasoning benchmarks, whether starting from scratch or fine-tuning from an existing model.