Fine-grained Visual Classification with High-temperature Refinement and Background Suppression

#FGVC

https://arxiv.org/abs/2303.06442

Fine-grained Visual Classification with High-temperature Refinement and Background Suppression

Fine-grained visual classification is a challenging task due to the high similarity between categories and distinct differences among data within one single category. To address the challenges, previous strategies have focused on localizing subtle discrepancies between categories and enhencing the discriminative features in them. However, the background also provides important information that can tell the model which features are unnecessary or even harmful for classification, and models that rely too heavily on subtle features may overlook global features and contextual information. In this paper, we propose a novel network called ``High-temperaturE Refinement and Background Suppression'' (HERBS), which consists of two modules, namely, the high-temperature refinement module and the background suppression module, for extracting discriminative features and suppressing background noise, respectively. The high-temperature refinement module allows the model to learn the appropriate feature scales by refining the features map at different scales and improving the learning of diverse features. And, the background suppression module first splits the features map into foreground and background using classification confidence scores and suppresses feature values in low-confidence areas while enhancing discriminative features. The experimental results show that the proposed HERBS effectively fuses features of varying scales, suppresses background noise, discriminative features at appropriate scales for fine-grained visual classification.The proposed method achieves state-of-the-art performance on the CUB-200-2011 and NABirds benchmarks, surpassing 93% accuracy on both datasets. Thus, HERBS presents a promising solution for improving the performance of fine-grained visual classification tasks. code: https://github.com/chou141253/FGVC-HERBS

arXiv.org

Working on an interesting fine-grained problem, but the #ICCV2023 deadline is approaching too quickly?
The FGVC10 paper submission website is now open!

https://sites.google.com/view/fgvc10/submission

Deadline: March 20

#CVPR #CVPR2023 #FGVC

FGVC10 Workshop - Submission

Call for Papers Deadline for Submission - March 20, 2023 17:59 Pacific Standard Time Notification of Acceptance - April 19, 2023 Camera Ready - May 2, 2023 Workshop - June 18, 2023 Submission Website - https://cmt3.research.microsoft.com/FGVC2023 For information about whether the workshop will be

Great News! FGVC10 (10th Workshop on Fine-Grained Visual Categorization) was accepted to CVPR 2023 and will take place in Vancouver. Interested in fine-grained learning and its applications? Consider submitting your paper to FGVC10.

Deadline: March 20.

More information on the website: https://sites.google.com/view/fgvc10

#CVPR #CVPR2023 #FGVC

FGVC10 Workshop

Announcements Paper submission deadline is the 20h of March 2023. More information here. FGVC10 will be held in conjunction with CVPR 2023 in Vancouver. More details to follow. Information about last year's workshop, FGVC9, is available here.