MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid

TL;DR

MuMIC leverages contrastively pretrained multimodal models with a tempered sigmoid loss for high-performance multi-label image classification, effectively handling noisy data and enabling zero-shot predictions.

Contribution

This paper introduces MuMIC, the first adaptation of contrastively learnt multimodal pretraining for real-world multi-label image classification tasks.

Findings

Achieved 85.6% GAP@10 on Booking.com images

Outperformed state-of-the-art models in multi-label classification

Supported zero-shot and domain-specific predictions

Abstract

Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representation learning abilities and is robust to natural distribution shifts. This success inspires us to leverage multimodal learning for multi-label classification tasks, and benefit from contrastively learnt pretrained models. We propose the Multimodal Multi-label Image Classification (MuMIC) framework, which utilizes a hardness-aware tempered sigmoid based Binary Cross Entropy loss function, thus enables the optimization on multi-label objectives and transfer learning on CLIP. MuMIC is capable of providing high classification performance, handling real-world noisy data, supporting zero-shot predictions, and producing domain-specific image embeddings. In this study, a total of 120 image classes are defined, and more than 140K positive annotations are collected on approximately 60K Booking.com images. The final MuMIC model is deployed on Booking.com Content Intelligence Platform, and it outperforms other state-of-the-art models with 85.6% GAP@10 and 83.8% GAP on all 120 classes, as well as a 90.1% macro mAP score across 32 majority classes. We summarize the modeling choices which are extensively tested through ablation studies. To the best of our knowledge, we are the first to adapt contrastively learnt multimodal pretraining for real-world multi-label image classification problems, and the innovation can be transferred to other domains.