Summary of Capeen: Image Captioning with Early Exits and Knowledge Distillation, by Divya Jyoti Bajpai and Manjesh Kumar Hanawal
CAPEEN: Image Captioning with Early Exits and Knowledge Distillation
by Divya Jyoti Bajpai, Manjesh Kumar Hanawal
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 a novel approach to improve the efficiency of deep neural networks (DNNs) in image captioning tasks. Specifically, they introduce CAPEEN, an Early Exit strategy that leverages knowledge distillation to adapt to varying levels of semantic information required for accurate predictions. This is achieved by inferring at intermediary layers if prediction confidence exceeds a predefined value learned from training data. Additionally, the authors present a variant called A-CAPEEN, which adapts the thresholds on the fly using the Multiarmed bandits framework to account for real-world deployments where target distributions may drift from those of training samples. Experimental results on the MS COCO and Flickr30k datasets show that CAPEEN gains a speedup of 1.77x while maintaining competitive performance compared to the final layer, and A-CAPEEN offers robustness against distortions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making image captioning faster and better! Scientists created a new way called CAPEEN that helps deep learning models work more efficiently. They did this by teaching the model to make decisions at different levels, depending on how sure it is about what it sees. This makes the process faster and just as good! The researchers also came up with another version called A-CAPEEN that can adjust itself for real-world situations where things might not be exactly like what they trained on. |
Keywords
» Artificial intelligence » Deep learning » Image captioning » Knowledge distillation
