Learning Visual Representations via Language-Guided Sampling

• URL: http://arxiv.org/abs/2302.12248v2
• Date: Wed, 29 Mar 2023 10:23:40 GMT
• Title: Learning Visual Representations via Language-Guided Sampling
• Authors: Mohamed El Banani, Karan Desai, Justin Johnson
• Abstract summary: We use language similarity to sample semantically similar image pairs for contrastive learning. Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity. We show that language-guided learning yields better features than image-based and image-text representation learning approaches.
• Score: 25.117909306792324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although an object may appear in numerous contexts, we often describe it in a limited number of ways. Language allows us to abstract away visual variation to represent and communicate concepts. Building on this intuition, we propose an alternative approach to visual representation learning: using language similarity to sample semantically similar image pairs for contrastive learning. Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of hand-crafted augmentations or learned clusters. Our approach also differs from image-text contrastive learning by relying on pre-trained language models to guide the learning rather than directly minimizing a cross-modal loss. Through a series of experiments, we show that language-guided learning yields better features than image-based and image-text representation learning approaches.

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