ASX-Dividend-Report-Banner

INFINIQ's AI Model Achieves Top Performance in Few-Shot Object Detection

April 12, 2024 02:00 AM AEST | By Cision
Follow us on Google News: https://kalkinemedia.com/resources/assets/public/images/google-news.webp
 INFINIQ's AI Model Achieves Top Performance in Few-Shot Object Detection
Image source: Kalkine Media

SAN JOSE, Calif., April 12, 2024 /PRNewswire/ -- INFINIQ, a leading South Korean AI platform services company, announced a breakthrough in few-shot object detection with the publication of their research paper, "Re-scoring using Image-Language Similarity for Few-Shot Object Detection," in the esteemed international journal, "Computer Vision and Image Understanding."

INFINIQ's RISF model outperforms conventional methods in object detection with minimal data
INFINIQ's RISF model outperforms conventional methods in object detection with minimal data

The paper explores the development of a novel AI model named RISF (Re-scoring using Image-Language Similarity for Few-Shot Object Detection). RISF addresses the challenge of accurately detecting objects in datasets containing limited images (less than 30). The model leverages the power of image-language similarity to pinpoint object location and classification.

RISF combines an object detection model with a Contrastive Language-Image Pre-training (CLIP) model. To ensure seamless integration and enhance accuracy, INFINIQ researchers developed a new loss function called Background Negative Re-scale Loss (BNRL).  

INFINIQ's RISF achieved recognition by securing the second position in the few-shot object detection category on "papers with code," a renowned platform for sharing AI research, achieving an average precision (AP) score of 25.5. The paper's publication in Computer Vision and Image Understanding, a high-caliber SCI(E)-indexed journal, further validates the model's significance.

"RISF outperforms conventional methods in object detection with minimal data," said Min Jae Jung, lead researcher behind the paper. "This exceptional performance and accuracy make RISF a valuable model during the AI learning phase."

"The publication of RISF in such a well-respected journal signifies its global potential," commented Jun Hyung Park, CEO of INFINIQ. "INFINIQ remains committed to driving innovation in the field of artificial intelligence."

About INFINIQ

INFINIQ, a member company of Global Digital Innovation Network (formerly known as Born2Global Centre), is a specialized artificial intelligence platform company with expertise in autonomous driving development and computer vision fields, serving a diverse clientele. With a focus on the AI development platform 'AI Studio' and the AI-based video analysis solution 'Heidi AI', the company is expanding its footprint into the autonomous driving, security, and defense sectors. Moreover, it has been diversifying its offerings since its inception in 2016, starting with AI data processing services, and now encompassing various services based on AI Vision and AI platforms.

 


Disclaimer

The content, including but not limited to any articles, news, quotes, information, data, text, reports, ratings, opinions, images, photos, graphics, graphs, charts, animations and video (Content) is a service of Kalkine Media Pty Ltd (“Kalkine Media, we or us”), ACN 629 651 672 and is available for personal and non-commercial use only. The principal purpose of the Content is to educate and inform. The Content does not contain or imply any recommendation or opinion intended to influence your financial decisions and must not be relied upon by you as such. Some of the Content on this website may be sponsored/non-sponsored, as applicable, but is NOT a solicitation or recommendation to buy, sell or hold the stocks of the company(s) or engage in any investment activity under discussion. Kalkine Media is neither licensed nor qualified to provide investment advice through this platform. Users should make their own enquiries about any investments and Kalkine Media strongly suggests the users to seek advice from a financial adviser, stockbroker or other professional (including taxation and legal advice), as necessary.
The content published on Kalkine Media also includes feeds sourced from third-party providers. Kalkine does not assert any ownership rights over the content provided by these third-party sources. The inclusion of such feeds on the Website is for informational purposes only. Kalkine does not guarantee the accuracy, completeness, or reliability of the content obtained from third-party feeds. Furthermore, Kalkine Media shall not be held liable for any errors, omissions, or inaccuracies in the content obtained from third-party feeds, nor for any damages or losses arising from the use of such content.
Kalkine Media hereby disclaims any and all the liabilities to any user for any direct, indirect, implied, punitive, special, incidental or other consequential damages arising from any use of the Content on this website, which is provided without warranties. The views expressed in the Content by the guests, if any, are their own and do not necessarily represent the views or opinions of Kalkine Media. Some of the images/music that may be used on this website are copyrighted to their respective owner(s). Kalkine Media does not claim ownership of any of the pictures displayed/music used on this website unless stated otherwise. The images/music that may be used on this website are taken from various sources on the internet, including paid subscriptions or are believed to be in public domain. We have made reasonable efforts to accredit the source wherever it was indicated as or found to be necessary.

This disclaimer is subject to change without notice. Users are advised to review this disclaimer periodically for any updates or modifications.

Recent Articles

Investing Tips

Previous Next
We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.