Summary of Clipartt: Adaptation Of Clip to New Domains at Test Time, by Gustavo Adolfo Vargas Hakim and David Osowiechi and Mehrdad Noori and Milad Cheraghalikhani and Ali Bahri and Moslem Yazdanpanah and Ismail Ben Ayed and Christian Desrosiers
CLIPArTT: Adaptation of CLIP to New Domains at Test Time
by Gustavo Adolfo Vargas Hakim, David Osowiechi, Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Moslem Yazdanpanah, Ismail Ben Ayed, Christian Desrosiers
First submitted to arxiv on: 1 May 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 Pre-trained vision-language models (VLMs) like CLIP have shown impressive adaptability across zero-shot classification tasks without additional training. However, their performance decreases in the presence of domain shifts. To address this issue, researchers introduce CLIP Adaptation duRing Test-Time (CLIPArTT), a fully test-time adaptation approach for CLIP. This method involves constructing automatic text prompts during inference and using them as pseudo-labels to re-classify inputs transductively. The authors also standardize TTA benchmarks in the realm of VLMs, demonstrating that CLIPArTT enhances performance dynamically across various datasets, including CIFAR-100, CIFAR-100-C, ImageNet-C, and VisDA-C. This research highlights the potential for improving VLMs’ adaptability through novel test-time strategies, offering insights for robust performance across varied datasets and environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer model that can understand both pictures and words. It’s like having a super-smart librarian who can find the right book based on what you’re describing. These models are really good at guessing what something is without being trained beforehand. But, if they encounter a new type of picture or description, their accuracy drops. Researchers created a new way to adapt this model so it can work better in different situations. They did this by generating new text prompts during the process of recognizing pictures and using them as clues to make more accurate guesses. This helps the model stay good at understanding pictures even when it encounters something new. The results show that this approach works well across many types of datasets. |
Keywords
» Artificial intelligence » Classification » Inference » Zero shot
