As the most fundamental scene understanding tasks, object detection and segmentation
have made tremendous progress in deep learning era. Due to the expensive manual …

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

Abstract The You Only Look Once (YOLO) series of detectors have established themselves
as efficient and practical tools. However their reliance on predefined and trained object …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Pre-trained vision-language models (VLMs) learn to align vision and language
representations on large-scale datasets, where each image-text pair usually contains a bag …

Deriving reliable region-word alignment from image-text pairs is critical to learnobject-level
vision-language representations for open-vocabulary object detection. Existing methods …

We present GLEE in this work an object-level foundation model for locating and identifying
objects in images and videos. Through a unified framework GLEEaccomplishes detection …

Referring image segmentation (RIS) aims to find a segmentation mask given a referring
expression grounded to a region of the input image. Collecting labelled datasets for this …

Object detection has been expanded from a limited number of categories to open
vocabulary. Moving forward, a complete intelligent vision system requires understanding …

Recent breakthroughs in vision-language models (VLMs) start a new page in the vision
community. The VLMs provide stronger and more generalizable feature embeddings …