Summary of Towards Unified Benchmark and Models For Multi-modal Perceptual Metrics, by Sara Ghazanfari et al.
Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics
by Sara Ghazanfari, Siddharth Garg, Nicolas Flammarion, Prashanth Krishnamurthy, Farshad Khorrami, Francesco Croce
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 investigates the challenge of developing automated metrics that accurately capture human perception of similarity across uni- and multimodal inputs. Researchers have applied general-purpose vision-language models, such as CLIP and large multi-modal models (LMMs), as zero-shot perceptual metrics with varying degrees of success. The study introduces UniSim-Bench, a comprehensive benchmark comprising 7 multi-modal perceptual similarity tasks and 25 datasets. Evaluation reveals that while general-purpose models perform reasonably well on average, they often lag behind specialized models on individual tasks. Conversely, task-specific metrics fail to generalize well to unseen, though related, tasks. To address this challenge, the authors fine-tune both encoder-based and generative vision-language models on a subset of UniSim-Bench tasks, achieving the highest average performance in some cases. However, these models still struggle with generalization to unseen tasks, highlighting the ongoing quest for a unified multi-task perceptual similarity metric that captures human perception. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how we can make computers understand what makes things similar or different. Right now, there’s no perfect way to do this because humans have very complex ideas about what makes things similar. The researchers made a test with 7 challenges and many datasets to see which methods work best. They found that some methods are good at one task but not others. To fix this, they tried adjusting the models for specific tasks and got better results in some cases. But even these improved models struggled when faced with new, similar challenges. The researchers want to create a method that can understand similarity in many different ways. |
Keywords
» Artificial intelligence » Encoder » Generalization » Multi modal » Multi task » Zero shot
