We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

We propose a dense object detector with an instance-wise sampling strategy, named IQDet.
Instead of using human prior sampling strategies, we first extract the regional feature of each …

Region sampling or weighting is significantly important to the success of modern region-
based object detectors. Unlike some previous works, which only focus on" hard" samples …

Abstract Non-Maximum Suppression (NMS) is an essential step of modern object detection
models for removing duplicated candidates. The efficacy of NMS heavily affects the final …

Transfer learning with pre-training on large-scale datasets has played an increasingly
significant role in computer vision and natural language processing recently. However, as …

In object detection, an intersection over union (IoU) threshold is required to define positives
and negatives. An object detector, trained with low IoU threshold, eg 0.5, usually produces …

Practical object detection application can lose its effectiveness on image inputs with natural
distribution shifts. This problem leads the research community to pay more attention on the …

Despite the prominent success of general object detection, the performance and efficiency of
Small Object Detection (SOD) are still unsatisfactory. Unlike existing works that struggle to …

The paradigm of large-scale pre-training followed by downstream fine-tuning has been
widely employed in various object detection algorithms. In this paper, we reveal …

Open-world object detection (OWOD) is a challenging computer vision problem, where the
task is to detect a known set of object categories while simultaneously identifying unknown …