Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic Losses
This addresses the problem of ambiguous object-concept correspondence in VLMs for researchers and practitioners, offering an incremental improvement through novel loss functions.
The paper tackles the problem of insufficient supervision in Vision-Language Models (VLMs) by proposing HIerarchically STructured Learning (HIST), which uses syntactic structures in text to enhance spatial vision-language alignment without additional human annotations. The method achieves up to +9.8% improvement in visual grounding and +6.3% in multi-object referring segmentation, with gains in downstream tasks like image-text retrieval (+1.1%) and visual question answering (+0.2%).
Vision-Language Models (VLMs) implicitly learn to associate image regions with words from large-scale training data, demonstrating an emergent capability for grounding concepts without dense annotations[14,18,51]. However, the coarse-grained supervision from image-caption pairs is often insufficient to resolve ambiguities in object-concept correspondence, even with enormous data volume. Rich semantic and syntactic structures within the text modality have been overlooked as sources of supervision. Starting from contrastive architectures (BLIP and ALBEF) that show strong intrinsic grounding abilities, we propose HIerarchically STructured Learning (HIST). HIST enhances spatial vision-language alignment without using additional human annotations, by hierarchically decomposing captions into the constituent Subjects, Phrases, and Composite Phrases, and enforcing entailment relation between a parent and its children in the hierarchy. Specifically, we introduce two novel loss functions: (1) Subject Loss, which aligns image content with the subject of the corresponding phrase, acting as an entailment of standard contrastive/matching losses at the Phrase level; (2) Composition Loss, to balance attention across multiple objects. HIST is general, and can be applied to any VLM for which attention between vision and language can be computed. Compared to baseline VLMs, HIST achieves up to +9.8% improvement in visual grounding and +6.3% in multi-object referring segmentation. Surprisingly, the improved spatial grounding leads to improvements in other downstream VLM tasks: +1.1% in image-text retrieval, and +0.2% in visual question answering.