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TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning

Joshua Feinglass, Yezhou Yang

TL;DR

TRaining-Free Object-Part Enhancement enriches a base caption with additional object-part details using object detector proposals and Natural Language Processing techniques, allowing seamless integration with other captioning methods and offering users enhanced flexibility.

Abstract

Zero-shot inference, where pre-trained models perform tasks without specific training data, is an exciting emergent ability of large models like CLIP. Although there has been considerable exploration into enhancing zero-shot abilities in image captioning (IC) for popular datasets such as MSCOCO and Flickr8k, these approaches fall short with fine-grained datasets like CUB, FLO, UCM-Captions, and Sydney-Captions. These datasets require captions to discern between visually and semantically similar classes, focusing on detailed object parts and their attributes. To overcome this challenge, we introduce TRaining-Free Object-Part Enhancement (TROPE). TROPE enriches a base caption with additional object-part details using object detector proposals and Natural Language Processing techniques. It complements rather than alters the base caption, allowing seamless integration with other captioning methods and offering users enhanced flexibility. Our evaluations show that TROPE consistently boosts performance across all tested zero-shot IC approaches and achieves state-of-the-art results on fine-grained IC datasets.

TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning

TL;DR

TRaining-Free Object-Part Enhancement enriches a base caption with additional object-part details using object detector proposals and Natural Language Processing techniques, allowing seamless integration with other captioning methods and offering users enhanced flexibility.

Abstract

Zero-shot inference, where pre-trained models perform tasks without specific training data, is an exciting emergent ability of large models like CLIP. Although there has been considerable exploration into enhancing zero-shot abilities in image captioning (IC) for popular datasets such as MSCOCO and Flickr8k, these approaches fall short with fine-grained datasets like CUB, FLO, UCM-Captions, and Sydney-Captions. These datasets require captions to discern between visually and semantically similar classes, focusing on detailed object parts and their attributes. To overcome this challenge, we introduce TRaining-Free Object-Part Enhancement (TROPE). TROPE enriches a base caption with additional object-part details using object detector proposals and Natural Language Processing techniques. It complements rather than alters the base caption, allowing seamless integration with other captioning methods and offering users enhanced flexibility. Our evaluations show that TROPE consistently boosts performance across all tested zero-shot IC approaches and achieves state-of-the-art results on fine-grained IC datasets.
Paper Structure (21 sections, 2 equations, 5 figures, 4 tables, 1 algorithm)

This paper contains 21 sections, 2 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Image Captioning Task
  5. Object Detectors in Image Captioning
  6. Measures of Object Proposal Similarity
  7. Structured Enhancement
  8. Overview
  9. Extract Key Objects from Base Caption ($h$)
  10. Generate Semantic Proposals ($g$)
  11. Aggregate and Add Semantic Proposals to Caption ($m$)
  12. Experiments
  13. Datasets
  14. Evaluation Metrics
  15. Fine-Grained Captioning Benchmark
  16. ...and 6 more sections

Figures (5)

  • Figure 1: An example differentiating TROPE from prior work in image caption enhancement, which substitute existing words in the sentence with more contextually appropriate alternatives. TROPE instead inserts supplemental information after key objects by mapping nouns to a region of the image and constructing semantic part proposals based on object parts and attributes found within this region.
  • Figure 2: A high-level visualization of the TROPE methodology expanded upon in Algorithm \ref{['alg:trope']}. Detailed descriptions of each TROPE function block can be found in their corresponding sections.
  • Figure 3: Precision-recall curves generated by sweeping the number of semantic proposals added to the base caption from 1 to 10 for both the Oscar and ConZIC base captions. Horizontal lines represent the base caption precision performance.
  • Figure 4: Qualitative examples of TROPE applied to captions generated by GPT4 gpt2023 with $N=5$ semantic part proposals. Minor failures can be observed in the 2nd image caption with erroneous attributes like "green" house and redundant parts like "house" and "building". The 3rd caption is another failure case where no supplemental information from TROPE is added to the caption since the base caption contains no key objects detected by VinVL.
  • Figure 5: Visualizations showcasing the unique characteristics of fine-grained datasets. The top plot shows the frequency of the 5 most common terms in our selected fine-grained and general domain datasets. The bottom plot shows the frequency of different semantic indicators across our selected datasets for both human annotations and available base captions from ConZIC.