Camera-based 3D object detection in BEV (Bird's Eye View) space has drawn great
attention over the past few years. Dense detectors typically follow a two-stage pipeline by …

Query-based object detectors directly decode image features into object instances with a set
of learnable queries. These query vectors are progressively refined to stable meaningful …

Benefitting from the development of pyramidal feature learning, current state-of-the-art multi-
scale detection paradigm has become proficient in detecting objects of varying scales …

Existing methods enhance the training of detection transformers by incorporating an
auxiliary one-to-many assignment. In this work, we treat the model as a multi-task …

Detection Transformer (DETR) predicts object bounding boxes and classes from learned
object queries. However, DETR exhibits three major flaws:(1) Only a subset of object queries …

High-resolution imagery captured by drones can detect critical components on high-voltage
transmission towers, providing inspection personnel with essential maintenance insights …

Recognition and generation are two fundamental tasks in computer vision, which are often
investigated separately in the exiting literature. However, these two tasks are highly …

DEtection TRansformer (DETR) becomes a dominant paradigm, mainly due to its common
architecture with high accuracy and no post-processing. However, DETR suffers from …

Benefiting from attention mechanisms, query-based detectors have a strong model capacity.
They predict classification and regression by utilizing their shared queries and features in …

Abstract In recent times, Detection Transformer (DETR) has been introduced and has shown
excellent performance in object detection. As a result, there has been an increase in DETR …