# Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing

arXiv: https://arxiv.org/abs/1509.08075 · HTML: https://mlsanity.com/paper/1509.08075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [Paraphrase Acquisition from Image Captions](https://mlsanity.com/paper/2301.11030/) (2023)

## Abstract (arXiv)

We introduce Segment-Phrase Table (SPT), a large collection of bijective associations between textual phrases and their corresponding segmentations. Leveraging recent progress in object recognition and natural language semantics, we show how we can successfully build a high-quality segment-phrase table using minimal human supervision. More importantly, we demonstrate the unique value unleashed by this rich bimodal resource, for both vision as well as natural language understanding. First, we show that fine-grained textual labels facilitate contextual reasoning that helps in satisfying semantic constraints across image segments. This feature enables us to achieve state-of-the-art segmentation results on benchmark datasets. Next, we show that the association of high-quality segmentations to textual phrases aids in richer semantic understanding and reasoning of these textual phrases. Leveraging this feature, we motivate the problem of visual entailment and visual paraphrasing, and demonstrate its utility on a large dataset.