New framework breaks down language into components for more precise object detection

Current vision-language models struggle when asked to identify objects using intricate descriptions that combine attributes and spatial relationships. A new approach called TaSe (Talk in Pieces, See in Whole) tackles this limitation by decomposing language into its fundamental building blocks.

How it works#

The framework rests on a straightforward idea: rather than processing entire sentences as monolithic units, the system breaks down text into three core components—objects, attributes, and relations. These disentangled pieces are then reassembled into hierarchically structured representations that preserve the logical flow of the original description.

Key components#

The researchers developed three main pieces for their solution:

• A hierarchical training dataset spanning three levels of complexity, from simple category labels to full descriptive sentences
• A disentanglement module that separates text embeddings into distinct subspace compositions, guided by a custom loss function designed for this decomposition task
• Hierarchical aggregation that combines these separated components back together, using specialized objectives to maintain their structured relationships

Results and impact#

Testing on the OmniLabel benchmark revealed a 24% improvement in performance, suggesting that explicitly modeling how language components relate to one another significantly enhances how AI systems interpret complex visual queries. This finding underscores the value of treating language as compositional—where meaning emerges from how individual elements combine rather than from treating entire phrases as indivisible units.

The work represents an arXiv submission advancing multimodal perception through more sophisticated handling of linguistic structure.