Summary of Barking Up the Syntactic Tree: Enhancing Vlm Training with Syntactic Losses, by Jiayun Luo et al.
Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic Losses
by Jiayun Luo, Mir Rayat Imtiaz Hossain, Boyang Li, Leonid Sigal
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 In this paper, researchers propose Hierarchically Structured Learning (HIST) to enhance Vision-Language Models (VLMs) for various tasks such as image-text retrieval and visual question answering. HIST decomposes captions into constituent components like subjects, noun phrases, and composite phrases, allowing for entailment-based regularization constraints on VLM attention maps. The paper introduces two novel loss functions: Subject Loss and Addition Loss. These losses improve VLM performance by up to 9.8% in visual grounding, 6.3% in multi-object referring segmentation, 1.1% in image-text retrieval, and 0.2% in visual question answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re teaching a machine learning course for people who don’t specialize in the field. You want to explain what this paper is about and why it’s important. The researchers created a new way to train Vision-Language Models called Hierarchically Structured Learning, or HIST. This method helps VLMs understand images better by breaking down captions into smaller parts like subjects and objects. By doing this, the model can learn to focus on the right things in an image. The paper shows that HIST works well for different tasks like finding specific objects in an image. |
Keywords
» Artificial intelligence » Attention » Grounding » Machine learning » Question answering » Regularization
