GoldiCLIP: The Goldilocks Approach for Balancing Explicit Supervision for Language-Image Pretraining

• URL: http://arxiv.org/abs/2603.24804v1
• Date: Wed, 25 Mar 2026 20:33:25 GMT
• Title: GoldiCLIP: The Goldilocks Approach for Balancing Explicit Supervision for Language-Image Pretraining
• Abstract summary: We present GoldiCLIP, a framework built on a Goldilocks principle of finding the right balance of supervision signals.<n>Our multifaceted training framework combines three key innovations: (1) a text-conditioned self-distillation method to align both text-agnostic and text-conditioned features; (2) an encoder integrated decoder with Visual Question Answering (VQA) objective that enables the encoder to generalize beyond the caption-like queries.
• Score: 16.806385071639866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Until recently, the success of large-scale vision-language models (VLMs) has primarily relied on billion-sample datasets, posing a significant barrier to progress. Latest works have begun to close this gap by improving supervision quality, but each addresses only a subset of the weaknesses in contrastive pretraining. We present GoldiCLIP, a framework built on a Goldilocks principle of finding the right balance of supervision signals. Our multifaceted training framework synergistically combines three key innovations: (1) a text-conditioned self-distillation method to align both text-agnostic and text-conditioned features; (2) an encoder integrated decoder with Visual Question Answering (VQA) objective that enables the encoder to generalize beyond the caption-like queries; and (3) an uncertainty-based weighting mechanism that automatically balances all heterogeneous losses. Trained on just 30 million images, 300x less data than leading methods, GoldiCLIP achieves state-of-the-art among data-efficient approaches, improving over the best comparable baseline by 2.2 points on MSCOCO retrieval, 2.0 on fine-grained retrieval, and 5.9 on question-based retrieval, while remaining competitive with billion-scale models. Project page: https://petsi.uk/goldiclip.

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