Image Captioning is the task of translating an input image into a textual description. As such,
it connects Vision and Language in a generative fashion, with applications that range from …

Image-text matching is gaining a leading role among tasks involving the joint understanding
of vision and language. In literature, this task is often used as a pre-training objective to …

The attention-based networks have become prevailing recently in visual question answering
(VQA) due to their high performances. However, the extensive memory consumption of …

Visual question answering (VQA) has been intensively studied as a multimodal task,
requiring efforts to bridge vision and language for correct answer inference. Recent attempts …

This paper addresses the task of inferring trip profiles (TPs), which consists of determining
the profile of travelers engaged in a particular trip given a set of possible categories. TPs …

Image captioning models have lately shown impressive results when applied to standard
datasets. Switching to real-life scenarios, however, constitutes a challenge due to the larger …

Recent advancements in deep learning have significantly enhanced content-based retrieval
methods, notably through models like CLIP that map images and texts into a shared …

Modern applications increasingly demand flexible computer vision models that adapt to
novel concepts not encountered during training. This necessity is pivotal in emerging …

Attention mechanism and Transformer-based architectures have recently revolutionized the
artificial intelligence landscape in almost every field. Ever since their first introduction, they …

针对跨模态检索任务中图像与文本模态的语义特征复杂度不一致问题, 提出了一种局部细粒度
对齐与全局特征推理相结合的图文匹配方法. 首先将图像和文本特征输入自适应交叉注意网络 …