6th Place Solution to Google Universal Image Embedding

2022-10-17 · S. Gkelios, A. Kastellos, S. Chatzichristofis

This paper presents the 6th place solution to the Google Universal Image Embedding competition on Kaggle. Our approach is based on the CLIP architecture, a powerful pre-trained model used to learn visual representation from natural language supervision. We also utilized the SubCenter ArcFace loss with dynamic margins to improve the distinctive power of class separability and embeddings. Finally, a diverse dataset has been created based on the test's set categories and the leaderboard's feedback. By carefully crafting a training scheme to enhance transfer learning, our submission scored 0.685 on the private leaderboard.

📄 PDF Abstract BibTeX arXiv:2210.09377

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Transfer Learning

Methods 이 논문이 사용한 방법론

CLIP Contrastive Language-Image Pre-training (CLIP), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning…

ArcFace ArcFace, or Additive Angular Margin Loss, is a loss function used in face recognition tasks. The softmax is traditionally used…

1st Place Solution in Google Universal Images Embedding

2022-10-16 · Shihao Shao, Qinghua Cui

This paper presents the 1st place solution for the Google Universal Images Embedding Competition on Kaggle. The highlighted part of our solution is based on 1) A novel way to conduct training and fine-tuning; 2) The idea…

3rd Place Solution for Google Universal Image Embedding

2022-10-14 · Nobuaki Aoki, Yasumasa Namba

This paper presents the 3rd place solution to the Google Universal Image Embedding Competition on Kaggle. We use ViT-H/14 from OpenCLIP for the backbone of ArcFace, and trained in 2 stage. 1st stage is done with freezed …

5th Place Solution to Kaggle Google Universal Image Embedding Competition

2022-10-18 · Noriaki Ota, Shingo Yokoi, Shinsuke Yamaoka

In this paper, we present our solution, which placed 5th in the kaggle Google Universal Image Embedding Competition in 2022. We use the ViT-H visual encoder of CLIP from the openclip repository as a backbone and train a …

2nd Place Solution to Google Universal Image Embedding

2022-10-17 · Xiaolong Huang, Qiankun Li

Image representations are a critical building block of computer vision applications. This paper presents the 2nd place solution to the Google Universal Image Embedding Competition, which is part of the ECCV2022 instance-…

Fine-Grained Image Classification image-classification Image Classification

General Image Descriptors for Open World Image Retrieval using ViT CLIP

2022-10-20 · Marcos V. Conde, Ivan Aerlic, Simon Jégou

The Google Universal Image Embedding (GUIE) Challenge is one of the first competitions in multi-domain image representations in the wild, covering a wide distribution of objects: landmarks, artwork, food, etc. This is a …

Image Retrieval Retrieval Zero-Shot Image Classification Zero-shot Image Retrieval+1