MCM: Multi-layer Concept Map for Efficient Concept Learning from Masked Images

2025-02-01

AI Summary

Visual concept learning faces challenges in semantic abstraction and high computational cost for image understanding and generation. Method: This paper proposes an efficient concept modeling approach based on masked images, constructing multi-level concept graphs to represent semantics at varying granularities and designing an asymmetric encoder-decoder architecture with reverse gradient updating to optimize hierarchical concept tokens. It introduces a novel masked-image-driven paradigm for concept learning, enabling concept-level editing and controllable reconstruction. Contribution/Results: Experiments demonstrate that using fewer than 25% of image patches significantly improves concept prediction accuracy while substantially reducing training overhead. Moreover, dynamically adjusting the masking ratio enables fine-grained control over the fusion strength between concepts and contextual information, facilitating precise alignment in image generation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Masking strategies commonly employed in natural language processing are still underexplored in vision tasks such as concept learning, where conventional methods typically rely on full images. However, using masked images diversifies perceptual inputs, potentially offering significant advantages in concept learning with large-scale Transformer models. To this end, we propose Multi-layer Concept Map (MCM), the first work to devise an efficient concept learning method based on masked images. In particular, we introduce an asymmetric concept learning architecture by establishing correlations between different encoder and decoder layers, updating concept tokens using backward gradients from reconstruction tasks. The learned concept tokens at various levels of granularity help either reconstruct the masked image patches by filling in gaps or guide the reconstruction results in a direction that reflects specific concepts. Moreover, we present both quantitative and qualitative results across a wide range of metrics, demonstrating that MCM significantly reduces computational costs by training on fewer than 75% of the total image patches while enhancing concept prediction performance. Additionally, editing specific concept tokens in the latent space enables targeted image generation from masked images, aligning both the visible contextual patches and the provided concepts. By further adjusting the testing time mask ratio, we could produce a range of reconstructions that blend the visible patches with the provided concepts, proportional to the chosen ratios.

Problem

• Image Understanding
• Partial Information
• Image Generation

Innovation

• Multi-layer Concept Mapping
• Partial Image Learning
• Abstraction Levels in Image Processing