Abstract Large-scale Vision-and-Language (V+ L) pre-training for representation learning
has proven to be effective in boosting various downstream V+ L tasks. However, when it …

The learning objective of vision-language approach of CLIP does not effectively account for
the noisy many-to-many correspondences found in web-harvested image captioning …

Existing multilingual video corpus moment retrieval (mVCMR) methods are mainly based on
a two-stream structure. The visual stream utilizes the visual content in the video to estimate …

In this work, we present a post-processing solution to address the hubness problem in cross-
modal retrieval, a phenomenon where a small number of gallery data points are frequently …

Owing to the effectiveness of cross-modal attentions, text-vision BERT models have
achieved excellent performance in text-image retrieval. Nevertheless, cross-modal …

Exploiting cross-modal attention on image region features and text features, cross-modal
BERT models have achieved higher accuracy than the embedding-based methods in cross …

Image-text retrieval requires the system to bridge the heterogenous gap between vision and
language for accurate retrieval while keeping the network lightweight-enough for efficient …

Visual content in multimedia advertising effectively attracts the customer's attention. Search-
based multimedia advertising is a cross-modal retrieval problem. Due to the modal gap …

Image-text retrieval is a fundamental task in bridging the semantics between vision and
language. The key challenge lies in accurately and efficiently learning the semantic …

We propose Texture BERT, a model describing visual attributes of texture using natural
language. To capture the rich details in texture images, we propose a group-wise compact …