UNITER: UNiversal Image-TExt Representation Learning

TL;DR

UNITER introduces a large-scale pre-trained model for joint image-text understanding, achieving state-of-the-art results across multiple vision-and-language tasks by employing novel conditional masking and fine-grained alignment techniques.

Contribution

The paper presents UNITER, a universal model for image-text representation learning with innovative pre-training tasks and alignment methods, significantly improving performance on diverse V+L tasks.

Findings

UNITER achieves new state-of-the-art results on six V+L tasks.

Conditional masking and OT-based WRA improve pre-training effectiveness.

Extensive ablations identify optimal pre-training task combinations.

Abstract

Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design four pre-training tasks: Masked Language Modeling (MLM), Masked Region Modeling (MRM, with three variants), Image-Text Matching (ITM), and Word-Region Alignment (WRA). Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). In addition to ITM for global image-text alignment, we also propose WRA via the use of Optimal Transport (OT) to explicitly encourage fine-grained alignment between words and image regions during pre-training. Comprehensive analysis shows that both conditional masking and OT-based WRA contribute to better pre-training. We also conduct a thorough ablation study to find an optimal combination of pre-training tasks. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks (over nine datasets), including Visual Question Answering, Image-Text Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning, Visual Entailment, and NLVR$^2$. Code is available at https://github.com/ChenRocks/UNITER.