In this paper, we study the challenging instance-wise vision-language tasks, where the free-
form language is required to align with the objects instead of the whole image. To address …

This paper presents a detailed study of improving vision features and develops an improved
object detection model for vision language (VL) tasks. Compared to the most widely used …

Task-oriented object detection aims to find objects suitable for accomplishing specific tasks.
As a challenging task, it requires simultaneous visual data processing and reasoning under …

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 …

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 …

Building robust and generic object detection frameworks requires scaling to larger label
spaces and bigger training datasets. However, it is prohibitively costly to acquire annotations …

This paper presents DetCLIPv2, an efficient and scalable training framework that
incorporates large-scale image-text pairs to achieve open-vocabulary object detection …

Abstract We present Contrastive Feature Masking Vision Transformer (CFM-ViT)-an image-
text pretraining methodology that achieves simultaneous learning of image-and region level …

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 …

The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However the progress in vision and vision …