Introduces group matching score for better evaluation of compositional reasoning and Test-Time Matching (TTM) algorithm for unsupervised self-improvement in multimodal models, achieving SOTA gains including surpassing GPT-4.1 and estimated human performance.
Why is winoground hard? investigating failures in visuolinguistic compositionality
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MACCO applies cross-modal masked reconstruction of compositional concepts with inter- and intra-modal auxiliary objectives to improve visio-linguistic compositionality in VLMs.
Empirical study shows bidirectional but sensitive relationship between compositionality and long-caption understanding in VLMs, promoted by high-quality grounded data and affected by architectural choices like frozen positional embeddings.
citing papers explorer
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Test-Time Matching: Unlocking Compositional Reasoning in Multimodal Models
Introduces group matching score for better evaluation of compositional reasoning and Test-Time Matching (TTM) algorithm for unsupervised self-improvement in multimodal models, achieving SOTA gains including surpassing GPT-4.1 and estimated human performance.
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Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality
MACCO applies cross-modal masked reconstruction of compositional concepts with inter- and intra-modal auxiliary objectives to improve visio-linguistic compositionality in VLMs.
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Long Story Short: Disentangling Compositionality and Long-Caption Understanding in Contrastive VLMs
Empirical study shows bidirectional but sensitive relationship between compositionality and long-caption understanding in VLMs, promoted by high-quality grounded data and affected by architectural choices like frozen positional embeddings.