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

In-context prompting in large language models (LLMs) has become a prevalent approach to
improve zero-shot capabilities but this idea is less explored in the vision domain. Existing …

We present a new open-vocabulary detection framework. Our framework uses both image-
level labels and detailed detection annotations when available. Our framework proceeds in …

We present T-Rex2, a highly practical model for open-set object detection. Previous open-
set object detection methods relying on text prompts effectively encapsulate the abstract …

The challenge of open-vocabulary recognition lies in the model has no clue of new
categories it is applied to. Existing works have proposed different methods to embed …

In this paper, we for the first time explore helpful multi-modal contextual knowledge to
understand novel categories for open-vocabulary object detection (OVD). The multi-modal …

This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection
models developed by IDEA Research, which aims to advance the" Edge" of open-set object …

The recent Segment Anything Model (SAM) has emerged as a new paradigmatic vision
foundation model, showcasing potent zero-shot generalization and flexible prompting …

Class-incremental object detection (CIOD) is a real-world desired capability, requiring an
object detector to continuously adapt to new tasks without forgetting learned ones, with the …

Open-vocabulary detection is a challenging task due to the requirement of detecting objects
based on class names, including those not encountered during training. Existing methods …