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 …

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 …

Recent advances in neural camera imaging pipelines have demonstrated notable progress.
Nevertheless, the real-world imaging pipeline still faces challenges including the lack of joint …

Recently, mobile AI agents based on VLMs have been gaining increasing attention. These
works typically utilize VLM as a foundation, fine-tuning it with instruction-based mobile …

In this paper, we introduce DINO-X, which is a unified object-centric vision model developed
by IDEA Research with the best open-world object detection performance to date. DINO-X …

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 …

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 …

We explore multi-modal contextual knowledge learned through multi-modal masked
language modeling to provide explicit localization guidance for novel classes in open …