Recently, Large Language Models (LLMs) and Multimodal Large Language Models
(MLLMs) have shown promise in instruction following and 2D image understanding. While …

This paper presents EdgeSAM, an accelerated variant of the Segment Anything Model
(SAM), optimized for efficient execution on edge devices with minimal compromise in …

This paper introduces the Stream Query Denoising (SQD) strategy, a novel and general
approach for high-definition map (HD-map) construction. SQD is designed to improve the …

Visual grounding is a common vision task that involves grounding descriptive sentences to
the corresponding regions of an image. Most existing methods use independent image-text …

Abstract Knowledge distillation (KD) has been one of the most potential methods to
implement a lightweight detector, which plays a significant role in satellite in-orbit processing …

DETR is a novel end-to-end transformer architecture object detector which significantly
outperforms classic detectors when scaling up. In this paper we focus on the compression of …

Deep neural networks have consistently represented the state of the art in most computer
vision problems. In these scenarios, larger and more complex models have demonstrated …

We introduce D-FINE, a powerful real-time object detector that achieves outstanding
localization precision by redefining the bounding box regression task in DETR models. D …

Large-scale image models have made great progress in recent years, pushing the
boundaries of many vision tasks, eg, object detection. Considering that deploying large …

Detection Transformer (DETR) and its variants have emerged a new paradigm to object
detection, but their high computational cost hinders practical applications. By investigating …