Abstract Large Multimodal Models (LMMs) extend Large Language Models to the vision
domain. Initial LMMs used holistic images and text prompts to generate ungrounded textual …

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

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

We introduce MQ-Det, an efficient architecture and pre-training strategy design to utilize both
textual description with open-set generalization and visual exemplars with rich description …

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable
of recognizing objects from both base and novel categories. Recent advances leverage …

Prompt learning has emerged as a valuable technique in enhancing vision-language
models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly …

In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in
improving embodied decision-making processes for agents. While Large Language Models …

Abstract Vision-Language Models (VLMs) demonstrate remarkable zero-shot generalization
to unseen tasks, but fall short of the performance of supervised methods in generalizing to …

Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set
of base categories on which the detection model is trained. Recent OVOD methods focus on …

Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light
of the widespread adoption of vision-based foundational models. Its primary objective is to …