Many tasks in smart agriculture have further requirements for the autonomous navigation of
agricultural robots. Due to irreplaceable visual information and low-cost hardware costs …

Deep learning approaches have recently raised the bar in many fields, from Natural
Language Processing to Computer Vision, by leveraging large amounts of data. However …

Combining simple architectures with large-scale pre-training has led to massive
improvements in image classification. For object detection, pre-training and scaling …

We present GLIPv2, a grounded VL understanding model, that serves both localization tasks
(eg, object detection, instance segmentation) and Vision-Language (VL) understanding …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

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 …

This paper presents a grounded language-image pre-training (GLIP) model for learning
object-level, language-aware, and semantic-rich visual representations. GLIP unifies object …

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

Automated visual understanding of our diverse and open world demands computer vision
models to generalize well with minimal customization for specific tasks, similar to human …

Recently, vision-language pre-training shows great potential in open-vocabulary object
detection, where detectors trained on base classes are devised for detecting new classes …