Summary of Enhance Vision-language Alignment with Noise, by Sida Huang et al.
Enhance Vision-Language Alignment with Noise
by Sida Huang, Hongyuan Zhang, Xuelong Li
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 whether pre-trained vision-language (VL) models can be fine-tuned using customized noise to enhance the alignment between visual and linguistic modalities in downstream tasks. The authors propose a new scheme, Positive-incentive Noise Injector (PiNI), which injects beneficial noise into both visual and text encoders to fine-tune CLIP-based few-shot classification tasks. By reformulating the inference process of CLIP and applying variational inference, PiNI generates beneficial noise that can be used to learn more diverse embeddings of vision and language, ultimately improving task-specific alignment within limited computational resources. The authors evaluate different noise incorporation approaches and network architectures of PiNI across 11 datasets, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to make computer models better understand pictures and words. Right now, we have some really good models that can do lots of things like identify objects in pictures or summarize text. But these models were trained on a lot of data and might not work as well when given new information they haven’t seen before. The researchers are trying to figure out how to make these models better at understanding the connection between pictures and words. They came up with an idea called PiNI (Positive-incentive Noise Injector) that adds special noise to the model’s training data to help it learn more about this connection. This noise helps the model pay attention to important details in both pictures and words, making it better at tasks like identifying objects or summarizing text. |
Keywords
» Artificial intelligence » Alignment » Attention » Classification » Few shot » Inference
