The editor of Downcodes will introduce you to SegVG, a new framework that solves the problem of target positioning in the field of AI vision! The traditional target positioning algorithm is like "myopia". It can only roughly select the target and cannot capture the details. SegVG breaks through this bottleneck. It uses pixel-level detailed information to make AI feel like wearing "high-definition glasses" and accurately identify every pixel of the target. This article will introduce the working principle, advantages and potential of SegVG in practical applications in a simple and easy-to-understand manner, and attach links to papers and codes to facilitate readers' in-depth study and research.
In the field of AI vision, target positioning has always been a difficult problem. The traditional algorithm is like "myopia". It can only roughly encircle the target with a "frame", but cannot clearly see the details inside. This is like describing a person to a friend and only giving a general height and body shape. It’s strange that your friend can find the person!
In order to solve this problem, a group of big guys from Illinois Institute of Technology, Cisco Research Institute and the University of Central Florida developed a new visual positioning framework called SegVG, claiming to make AI bid farewell to "myopia"!
The core secret of SegVG is: "pixel-level" details! The traditional algorithm only uses bounding box information to train AI, which is equivalent to only showing the AI a blurry shadow. SegVG converts the bounding box information into segmentation signals, which is equivalent to putting "high-definition glasses" on the AI, allowing the AI to see every pixel of the target clearly!
Specifically, SegVG adopts a "multi-layer multi-task encoder-decoder". The name sounds complicated, but you can actually think of it as a super-sophisticated "microscope" that contains queries for regression and multiple queries for segmentation. To put it simply, different "lenses" are used to perform bounding box regression and segmentation tasks respectively, and the target is repeatedly observed to extract more refined information.
What's even more powerful is that SegVG also introduces a "ternary alignment module", which is equivalent to equipping AI with a "translator" to specifically solve the problem of "language barrier" between model pre-training parameters and query embedding. Through the ternary attention mechanism, this "translator" can "translate" queries, text and visual features into the same channel, allowing AI to better understand target information.
What is the effect of SegVG? The experts conducted experiments on five commonly used data sets and found that the performance of SegVG defeated many traditional algorithms! Especially in the two notorious "difficulties" of RefCOCO+ and RefCOCOg "On the data set, SegVG has achieved breakthrough results!
In addition to precise positioning, SegVG can also output the confidence score of model predictions. To put it simply, the AI will tell you how confident it is in its judgment. This is very important in practical applications. For example, if you want to use AI to identify medical images, if the AI's confidence is not high, you will need to manually review it to avoid misdiagnosis.
The open source of SegVG is a major benefit to the entire AI vision field! I believe that more and more developers and researchers will join the SegVG camp in the future to jointly promote the development of AI vision technology.
Paper address: https://arxiv.org/pdf/2407.03200
Code link: https://github.com/WeitaiKang/SegVG/tree/main
All in all, the emergence of SegVG provides new ideas and methods for precise target positioning in the field of AI vision, and its open source also provides valuable learning and research resources for developers. I believe that the future development of SegVG will have a profound impact on AI vision technology and deserves our continued attention!