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In the computer vision domain, a fundamental problem is object recognition and classification. To recognize a single object, we need to train a model with images of that object class to make the model familiar with it. This requires annotating and labeling different classes manually, a labor-intensive process.
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20 hours ago · The task of detecting small and tiny objects has not shown significant performance improvement compared to detecting medium and large objects, even with ad.
22 hours ago · This paper studies zero-shot object recognition using event camera data. Guided by CLIP, which is pre-trained on RGB images, existing approaches achieve ...
4 hours ago · CNNs have revolutionized computer vision tasks by achieving remarkable accuracy in image classification, object detection, and segmentation. Their ability to ...
10 hours ago · 2D Object Detection. 403 benchmarks. 91 papers with code · Open Vocabulary Object Detection. 6 benchmarks. 66 papers with code · Thermal Image Segmentation. 7 ...
15 hours ago · Object detection is a vast domain which comprises of various methods of detecting objects efficiently and with good accuracy. Most of the images that we use ...
22 hours ago · Open-vocabulary object detection (OVD) [37] is receiving increasing attention due to its capability of detecting novel objects at test time.
15 hours ago · Deep learning-based object detection algorithms are widely applied in fields such as aerial image detection, medical image processing, precision agriculture, ...
16 hours ago · The image set for our object recognition project comprises very large PNG images, each exceeding 500MB, extracted from GeoTIFF files.
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