Object detection is a crucial branch of computer vision that aims to locate and classify
objects in images. Using deep convolutional neural networks (CNNs) as the primary …

Humans have long been recorded in a variety of forms since antiquity. For example,
sculptures and paintings were the primary media for depicting human beings before the …

Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

Semi-supervised learning is a promising method for medical image segmentation under
limited annotation. However, the model cognitive bias impairs the segmentation …

Diffusion models are generative models with impressive text-to-image synthesis capabilities
and have spurred a new wave of creative methods for classical machine learning tasks …

This paper presents a DETR-based method for cross-domain weakly supervised object
detection (CDWSOD), aiming at adapting the detector from source to target domain through …

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a
few target labeled images are available for training in addition to sufficient source labeled …

This paper proposes a novel method to improve the performance of a trained object detector
on scenes with fixed camera perspectives based on self-supervised adaptation. Given a …

Weakly-supervised object detection (WSOD), which requires only image-level annotations
for training detectors, has gained enormous attention. Despite recent rapid advance in …

In object detection, localization and classification of the targets are two fundamental
subtasks that underpin the application of many knowledge-based intelligent models in …