Summary of Q-groundcam: Quantifying Grounding in Vision Language Models Via Gradcam, by Navid Rajabi et al.
Q-GroundCAM: Quantifying Grounding in Vision Language Models via GradCAM
by Navid Rajabi, Jana Kosecka
First submitted to arxiv on: 29 Apr 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 This paper explores the abilities of Vision and Language Models (VLMs) to comprehend compositional scene understanding through zero-shot (ZS) performance across various tasks. Despite VLM advancements, recent probing studies have shown that even top-performing models struggle to accurately ground and localize linguistic phrases in images. To address this limitation, the authors introduce a novel suite of quantitative metrics using GradCAM activations to evaluate the grounding capabilities of pre-trained VLMs like CLIP, BLIP, and ALBEF. These metrics offer an explainable approach for comparing zero-shot performances and measuring models’ uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers can understand scenes by combining images and words. Right now, these computer models are very good at doing this, but they struggle to show where certain words refer to in the image. The authors want to know why this is happening and if there’s a way to make it better. They created new ways to measure how well these computer models do this task and found that bigger models with more training data can be better at understanding scenes. |
Keywords
» Artificial intelligence » Grounding » Scene understanding » Zero shot
