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

Despite significant progress in object categorization, in recent years, a number of important
challenges remain; mainly, ability to learn from limited labeled data and ability to recognize …

Despite great progress in object detection, most existing methods work only on a limited set
of object categories, due to the tremendous human effort needed for bounding-box …

Vision-language models such as CLIP have boosted the performance of open-vocabulary
object detection, where the detector is trained on base categories but required to detect …

Abstract We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility
function for CLIP's contrastive loss, intending to train an alignment between the patch tokens …

Abstract Contrastive Language-Image Pre-training (CLIP) has made a remarkable
breakthrough in open-vocabulary zero-shot image recognition. Many recent studies …

Contrastive language-image pre-training (CLIP) serves as a de-facto standard to align
images and texts. Nonetheless, the loose correlation between images and texts of web …

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 …

Open vocabulary object detection has been greately advanced by the recent development of
vision-language pre-trained model, which helps recognizing the novel objects with only …

Despite the remarkable accuracy of deep neural networks in object detection, they are costly
to train and scale due to supervision requirements. Particularly, learning more object …