Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding
Summary
Proposes Slipform, a training framework that uses lexical concreteness to select harder negatives and a margin-based Cement loss, boosting compositional reasoning in vision-language models.
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Paper page - Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding
Source: https://huggingface.co/papers/2604.13313
Abstract
Vision-language models face challenges in compositional reasoning due to insufficient samples for distinguishing subtle semantics, which are addressed through lexical concreteness-based negative sample selection and a novel margin-based loss function.
Vision-Language Modelsdemonstrate remarkable capabilities but often struggle withcompositional reasoning, exhibiting vulnerabilities regarding word order and attribute binding. This limitation arises from a scarcity of informative samples needed to differentiate subtle semantic variations duringcontrastive pretraining. Althoughhard negative miningoffers a promising remedy, existing methods lack explicit mechanisms to dictate which linguistic elements undergo modification. Instead of engineering generative architectures, this study establisheslexical concretenessas a fundamental determinant of negative sample efficacy. Modifying highly concrete terms generates more pronounced structural and visual discrepancies, providing a substantially stronger learning signal. Leveraging this principle, ConcretePlant is proposed to systematically isolate and manipulate perceptually grounded concepts. Analyses of theInfoNCEfurther reveals a severegradient imbalance, where easily distinguishable pairs disproportionately overwhelm the optimization process and restrict the bandwidth available for nuanced learning. To resolve this degradation, theCement lossis formulated utilizing a margin-based approach. By correlating psycholinguistic scores with sample difficulty, this objective dynamically calibrates the penalization applied to individual training pairs. Comprehensive evaluations substantiate these theoretical claims. The integrated framework, designated asSlipform, achieves state-of-the-art accuracy across diverse compositional evaluation benchmarks, general cross-modal retrieval, single and multi label linear probing.
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