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Found 8 papers, 7 papers with code
A Broad Dataset is All You Need for One-Shot Object Detection
We here show that this generalization gap can be nearly closed by increasing the number of object categories used during training.
Shortcut Learning in Deep Neural Networks
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence.
Optimizing Rank-based Metrics with Blackbox Differentiation
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models.
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
Ranked #1 on
Robust Object Detection
on MS COCO
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
One-Shot Instance Segmentation
We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult.
Ranked #1 on
One-Shot Instance Segmentation
on MS COCO
One-shot Texture Segmentation
We introduce one-shot texture segmentation: the task of segmenting an input image containing multiple textures given a patch of a reference texture.
One-Shot Segmentation in Clutter
We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example.
Ranked #1 on
One-Shot Segmentation
on Cluttered Omniglot
