Summary of Grit: Faster and Better Image Captioning Transformer Using Dual Visual Features, by Van-quang Nguyen et al.
GRIT: Faster and Better Image captioning Transformer Using Dual Visual Features
by Van-Quang Nguyen, Masanori Suganuma, Takayuki Okatani
First submitted to arxiv on: 20 Jul 2022
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel Transformer-based architecture, called GRIT (Grid- and Region-based Image captioning Transformer), for image captioning tasks. Unlike current state-of-the-art methods that rely on region-based features extracted by object detectors like Faster R-CNN, GRIT leverages both grid-based and region-based features to generate more accurate captions. The proposed model replaces traditional CNN-based detectors with DETR-based ones, making it computationally faster while maintaining high inference accuracy. Moreover, the monolithic design of GRIT enables end-to-end training of the model. Experimental results on several image captioning benchmarks demonstrate that GRIT outperforms previous methods in terms of both accuracy and speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want a computer to describe what’s happening in a picture. Current ways to do this use information about specific objects, like people or cars. However, these methods have some problems, like not understanding the context of the image well enough. This paper presents a new approach that uses two types of information: object-level details and overall visual cues. The result is a model that can accurately describe images while being efficient and easy to train. Tests show that this new method performs better than others in describing pictures and is faster too. |
Keywords
» Artificial intelligence » Cnn » Image captioning » Inference » Transformer
