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Found 6 papers, 2 papers with code
MobileNetV4 - Universal Models for the Mobile Ecosystem
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices.
Improving Object Detection with Selective Self-supervised Self-training
They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets.
Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection
Learning to localize and name object instances is a fundamental problem in vision, but state-of-the-art approaches rely on expensive bounding box supervision.
Learning to discover and localize visual objects with open vocabulary
To alleviate the cost of obtaining accurate bounding boxes for training today's state-of-the-art object detection models, recent weakly supervised detection work has proposed techniques to learn from image-level labels.
Quantized Kernel Learning for Feature Matching
Matching local visual features is a crucial problem in computer vision and its accuracy greatly depends on the choice of similarity measure.
Query Adaptive Similarity for Large Scale Object Retrieval
Furthermore, we propose a function to score the individual contributions into an image to image similarity within the probabilistic framework.
