Mutual feedback improves multimodal learning and visual grounding
Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback
Machine LearningComputer Vision and Pattern Recognition
Summary
Combining information from different sources, like images and text, can be tricky. The authors designed a system where two types of data talk back and forth repeatedly to better understand each other. This interaction helps create paired representations that capture details from both inputs more effectively. Their experiments show this approach improves tasks like classifying data and linking text to images. The system also refines visual details step-by-step by using clues from the other type of data.
What this means in practice
- •For computer vision engineers: Improve image and text joint understanding to enhance visual grounding accuracy in real-world datasets.
- •For machine learning engineers: Boost multimodal classification models by incorporating reciprocal feedback between different data types.
- •For advertising technology teams: Develop interactive systems that better connect text descriptions with images for targeted ad selection and display.$Commercial implications: Enables more accurate pairing of ads to content visuals, improving targeting and engagement in ad platforms.
Authors
Ho-min Park, Byungkon Kang
Abstract
This work proposes a mutual feedback architecture, MEQ, that refines the two inputs, of possibly different modalities, into a pair of coupled embeddings such that each embedding reflects the information of the other. The core idea is to incorporate continuous interchange of information between the two inputs. This idea leads to a mutual feedback architecture consisting of two components whose outputs are fed back into the other. The final output of this model is defined as the fixed point of this interaction. We provide theoretical analysis that offers interpretation of this model as well as design choices to prevent failure cases. We show the benefits of MEQ through classification and visual grounding tasks spanning various datasets. Quantitatively, our model outperforms or shows competitive performance on concatenation-based multimodal classification problems. Qualitatively, the proposed interactive mechanism allows the model to progressively refine the visual grounding when paired with complementary modality, thus demonstrating the power of mutual feedback under such settings.