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 …

The recognition capabilities of current state-of-the-art 3D models are limited by datasets with
a small number of annotated data and a pre-defined set of categories. In its 2D counterpart …

In this work, we present OpenSeeD, a simple Open-vocabulary Segmentation and Detection
framework that learns from different segmentation and detection datasets. To bridge the gap …

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 …

Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects
from novel categories beyond the base categories on which the detector is trained. Recent …

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

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

Recent advances in detecting arbitrary objects in the real world are trained and evaluated
on object detection datasets with a relatively restricted vocabulary. To facilitate the …

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

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable
of recognizing objects from both base and novel categories. Recent advances leverage …