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No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects
Convolutional neural networks (CNNs) have made resounding success in many computer
vision tasks such as image classification and object detection. However, their performance …
vision tasks such as image classification and object detection. However, their performance …
Simple open-vocabulary object detection
Combining simple architectures with large-scale pre-training has led to massive
improvements in image classification. For object detection, pre-training and scaling …
improvements in image classification. For object detection, pre-training and scaling …
Actionformer: Localizing moments of actions with transformers
Self-attention based Transformer models have demonstrated impressive results for image
classification and object detection, and more recently for video understanding. Inspired by …
classification and object detection, and more recently for video understanding. Inspired by …
Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications
In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are
usually developed. Such models demand high computational resources and therefore …
usually developed. Such models demand high computational resources and therefore …
RFLA: Gaussian receptive field based label assignment for tiny object detection
Detecting tiny objects is one of the main obstacles hindering the development of object
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …
A comprehensive survey and deep learning-based approach for human recognition using ear biometric
Human recognition systems based on biometrics are much in demand due to increasing
concerns of security and privacy. The human ear is unique and useful for recognition. It …
concerns of security and privacy. The human ear is unique and useful for recognition. It …
Pillarnet: Real-time and high-performance pillar-based 3d object detection
Real-time and high-performance 3D object detection is of critical importance for autonomous
driving. Recent top-performing 3D object detectors mainly rely on point-based or 3D voxel …
driving. Recent top-performing 3D object detectors mainly rely on point-based or 3D voxel …
End-to-end object detection with transformers
We present a new method that views object detection as a direct set prediction problem. Our
approach streamlines the detection pipeline, effectively removing the need for many hand …
approach streamlines the detection pipeline, effectively removing the need for many hand …
Reconet: Recurrent correction network for fast and efficient multi-modality image fusion
Recent advances in deep networks have gained great attention in infrared and visible image
fusion (IVIF). Nevertheless, most existing methods are incapable of dealing with slight …
fusion (IVIF). Nevertheless, most existing methods are incapable of dealing with slight …
Probabilistic anchor assignment with iou prediction for object detection
In object detection, determining which anchors to assign as positive or negative samples,
known as anchor assignment, has been revealed as a core procedure that can significantly …
known as anchor assignment, has been revealed as a core procedure that can significantly …