Summary of Multi-point Positional Insertion Tuning For Small Object Detection, by Kanoko Goto et al.
Multi-Point Positional Insertion Tuning for Small Object Detection
by Kanoko Goto, Takumi Karasawa, Takumi Hirose, Rei Kawakami, Nakamasa Inoue
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a novel approach to finetuning large-scale object detection models for small object detection tasks. The proposed method, called Multi-Point Positional Insertion (MPI) tuning, leverages recent advances in vision-language pretraining and parameter-efficient finetuning (PEFT). MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling efficient detection of small objects by providing precise positional information to latent features. Experimental results on the SODA-D dataset demonstrate the effectiveness of MPI, which performs comparably to conventional PEFT methods while significantly reducing the number of parameters that need to be tuned. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computer vision models work better for detecting small things in pictures. Right now, it’s hard to do this because we have to use really big and powerful computers to fine-tune the models. The researchers came up with an idea called Multi-Point Positional Insertion (MPI) that helps us do this more efficiently. MPI adds special information about where things are in a picture to the model, which makes it better at finding small objects. The results show that MPI works almost as well as other methods, but uses much fewer calculations and memory. |
Keywords
» Artificial intelligence » Object detection » Parameter efficient » Pretraining
