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Vision-language pre-training: Basics, recent advances, and future trends
This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …
intelligence that have been developed in the last few years. We group these approaches …
A comprehensive survey of deep learning for image captioning
Generating a description of an image is called image captioning. Image captioning requires
recognizing the important objects, their attributes, and their relationships in an image. It also …
recognizing the important objects, their attributes, and their relationships in an image. It also …
Imagebind: One embedding space to bind them all
We present ImageBind, an approach to learn a joint embedding across six different
modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …
modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …
[PDF][PDF] Large-scale domain-specific pretraining for biomedical vision-language processing
Contrastive pretraining on parallel image-text data has attained great success in vision-
language processing (VLP), as exemplified by CLIP and related methods. However, prior …
language processing (VLP), as exemplified by CLIP and related methods. However, prior …
Scaling up visual and vision-language representation learning with noisy text supervision
Pre-trained representations are becoming crucial for many NLP and perception tasks. While
representation learning in NLP has transitioned to training on raw text without human …
representation learning in NLP has transitioned to training on raw text without human …
Clip-forge: Towards zero-shot text-to-shape generation
Generating shapes using natural language can enable new ways of imagining and creating
the things around us. While significant recent progress has been made in text-to-image …
the things around us. While significant recent progress has been made in text-to-image …
Multi-modality cross attention network for image and sentence matching
The key of image and sentence matching is to accurately measure the visual-semantic
similarity between an image and a sentence. However, most existing methods make use of …
similarity between an image and a sentence. However, most existing methods make use of …
[HTML][HTML] Combined scaling for zero-shot transfer learning
Recent developments in multimodal training methodologies, including CLIP and ALIGN,
obviate the necessity for individual data labeling. These approaches utilize pairs of data and …
obviate the necessity for individual data labeling. These approaches utilize pairs of data and …
Deep multimodal representation learning: A survey
Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …
Stacked cross attention for image-text matching
In this paper, we study the problem of image-text matching. Inferring the latent semantic
alignment between objects or other salient stuff (eg snow, sky, lawn) and the corresponding …
alignment between objects or other salient stuff (eg snow, sky, lawn) and the corresponding …