Dynamic Adaptive Multispectral Detection Transformer for Infrared-Visible Object Detection
The author proposes the DAMS-DETR model to address challenges in infrared-visible object detection by dynamically selecting modality features and adaptively fusing complementary information.
Open-Vocabulary 3D Object Detection in Urban Environments
Introducing a novel approach, Find n’ Propagate, to enhance open-vocabulary 3D object detection in urban environments.
Co-Occurrence of Object Detection and Identification for Unlabeled Object Discovery
Proposing a deep learning approach for identifying co-occurring objects with base objects in multilabel categories.
Improved Blood Cell Detection with CST-YOLO: Leveraging YOLOv7 and CNN-Swin Transformer Fusion
This research paper introduces CST-YOLO, a novel object detection model specifically designed for small-scale objects like blood cells. By integrating a CNN-Swin Transformer module with an enhanced YOLOv7 architecture, CST-YOLO achieves superior detection accuracy compared to existing YOLO models and demonstrates the potential of CNN-Transformer fusion for improving small object detection.
Scale-Invariant Object Detection Using Depthwise Switchable Atrous Convolutional Network
This research paper introduces a novel depthwise switchable atrous convolutional network for object detection, enhancing the detection of objects at varying scales by dynamically adjusting atrous convolution rates and incorporating global context information.
Hyper-YOLO: Enhancing Object Detection with Hypergraph Computation for Improved Feature Correlation
Hyper-YOLO, a novel object detection method, leverages hypergraph computation to capture complex high-order correlations among visual features, significantly improving accuracy compared to traditional YOLO models.
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