Impact of Language Guidance: A Reproducibility Study

2025-04-10

AI Summary

Modern deep learning relies heavily on large-scale labeled data, yet manual annotation is costly and error-prone. While self-supervised contrastive learning (e.g., SimCLR, CLIP) avoids explicit labels, it often depends on image augmentations or cross-modal alignment, limiting semantic consistency. Banani et al. (2023) proposed language-guided view sampling to improve conceptual similarity, but we identify severe caption quality issues in their RedCaps dataset, critically degrading model performance. Method: We reproduce their framework, systematically diagnose RedCaps’ linguistic deficiencies, replace low-fidelity captions with high-fidelity image descriptions generated by BLIP-2, and introduce a novel Grad-CAM–based metric for semantic interpretability evaluation. Contribution/Results: Caption replacement yields a 5.2% absolute gain in linear probe accuracy. Our proposed metric effectively discriminates models by semantic representation fidelity, empirically validating that language guidance is only beneficial when grounded in semantically faithful captions.

Technology Category

• Computer Vision: Language and Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Multimodal Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Modern deep-learning architectures need large amounts of data to produce state-of-the-art results. Annotating such huge datasets is time-consuming, expensive, and prone to human error. Recent advances in self-supervised learning allow us to train huge models without explicit annotation. Contrastive learning is a popular paradigm in self-supervised learning. Recent works like SimCLR and CLIP rely on image augmentations or directly minimizing cross-modal loss between image and text. Banani et al. (2023) propose to use language guidance to sample view pairs. They claim that language enables better conceptual similarity, eliminating the effects of visual variability. We reproduce their experiments to verify their claims and find that their dataset, RedCaps, contains low-quality captions. We use an off-the-shelf image captioning model, BLIP-2, to replace the captions and improve performance, and we also devise a new metric to evaluate the semantic capabilities of self-supervised models based on interpretability methods.

Problem

• Evaluating language guidance impact on self-supervised learning reproducibility
• Addressing low-quality captions in RedCaps dataset using BLIP-2
• Developing new metric for semantic evaluation of self-supervised models

Innovation

• Use language guidance for sampling view pairs
• Replace captions with BLIP-2 for better quality
• Devise new metric for semantic capability evaluation