Despite the stunning ability to generate high-quality images by recent text-to-image models,
current approaches often struggle to effectively compose objects with different attributes and …

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 …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

In this paper, we formally address universal object detection, which aims to detect every
scene and predict every category. The dependence on human annotations, the limited …

We aim at advancing open-vocabulary object detection, which detects objects described by
arbitrary text inputs. The fundamental challenge is the availability of training data. It is costly …

Human activity recognition is essential in many domains, including the medical and smart
home sectors. Using deep learning, we conduct a comprehensive survey of current state …

The rapid advancement of deep learning models is often attributed to their ability to leverage
massive training data. In contrast such privilege has not yet fully benefited 3D deep learning …

In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO,
which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is …

Designing robust text-to-image (T2I) models have been extensively explored in recent years,
especially with the emergence of diffusion models, which achieves state-of-the-art results on …

Object localization in general environments is a fundamental part of vision systems. While
dominating on the COCO benchmark, recent Transformer-based detection methods are not …