… modal representations on image-text pairs are becoming popular for vision-language tasks.
… text features as input to the model to be pre-trained and use self-attention to learn image-text …

… convenient alternative method for their current meter reading updating system. The proposed
solution is to use computer vision techniques for capturing readings. One of the alternative …

… This framework overturned many computer science fields, like Computer Vision and Natural
Language Processing, obtaining astonishing results. Nevertheless, many challenges are …

… Inspired by the success of Transformers in NLP [20, 76], the Vision Transformer (ViT) [22]
was recently proposed and has been successfully applied to multiple computer vision tasks, …

… Fundamental to a successful computer vision approach for affective computing is the feature
representation of the image, and there are several approaches to engineering such features…

… -text retrieval. It is a significant step towards flexible and practical zero-shot classifiers for
computer vision … In this work, we concentrate on the continual vision-language representation …

… , provided by the Computer Vision Foundation. Except … Text Reconstruction We study the
effects of text reconstruction on monomodal and multimodal data. As shown in Table 8e, the text …

… This requires not only advanced computer vision and deep learning techniques but also …
This account consisted of 14 tweets, each with an image and an alt text caption. The tweets in …

… training the dual-encoder model on VideoCC with alttext. Specifically, training with only 1% …
∼7M), indicating that the original alttext scales poorly. We attribute the alt-text’s inferior perfor…

… relationships between human gaze, description, and computer vision. In: Proceedings of
the IEEE Conference on Computer Vision and Pattern Recognition, pp. 739–746 (2013) …