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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 1: AAAI-22 Technical Tracks 1

Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation

February 1, 2023

Authors

Chao Chen

School of Computer Science, Wuhan University JD Explore Academy Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence, Wuhan University National Engineering Research Center for Multimedia Software, Wuhan University


Yibing Zhan

JD Explore Academy


Baosheng Yu

The University of Sydney


Liu Liu

The University of Sydney


Yong Luo

School of Computer Science, Wuhan University


Bo Du

School of Computer Science, Wuhan University


Proceedings:

No. 1: AAAI-22 Technical Tracks 1

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision I

Downloads:

Download PDF

Abstract:

Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG methods usually suffer from sub-optimal scene graph generation because of the long-tailed distribution of training data. To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation. Specifically, RTPB uses a distributed-based prior bias to improve models' detecting ability on less frequent relationships during training, thus improving the model generalizability on tail categories. In addition, to further explore the contextual information of objects and relationships, we design a contextual encoding backbone network, termed as Dual Transformer (DTrans). We perform extensive experiments on a very popular benchmark, VG150, to demonstrate the effectiveness of our method for the unbiased scene graph generation. In specific, our RTPB achieves an improvement of over 10% under the mean recall when applied to current SGG methods. Furthermore, DTrans with RTPB outperforms nearly all state-of-the-art methods with a large margin. Code is available at https://github.com/ChCh1999/RTPB

DOI:

10.1609/aaai.v36i1.19896


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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