Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Connecting Vision and Language plays an essential role in Generative Intelligence. For this
reason, large research efforts have been devoted to image captioning, ie describing images …

Abstract We present Fast Language-Image Pre-training (FLIP), a simple and more efficient
method for training CLIP. Our method randomly masks out and removes a large portion of …

Building models that can be rapidly adapted to novel tasks using only a handful of annotated
examples is an open challenge for multimodal machine learning research. We introduce …

With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential
to investigate ways to adapt these models to downstream datasets. A recently proposed …

Grouping and recognition are important components of visual scene understanding, eg, for
object detection and semantic segmentation. With end-to-end deep learning systems …

Current object detectors are limited in vocabulary size due to the small scale of detection
datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as …

State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic
pretraining for obtaining good performance on a variety of downstream tasks. Generally …

Contrastive language-image pretraining (CLIP) using image-text pairs has achieved
impressive results on image classification in both zero-shot and transfer learning settings …

We combine neural rendering with multi-modal image and text representations to synthesize
diverse 3D objects solely from natural language descriptions. Our method, Dream Fields …