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Found 9 papers, 4 papers with code
Proper Reuse of Image Classification Features Improves Object Detection
A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task.
Less is More: Generating Grounded Navigation Instructions from Landmarks
We study the automatic generation of navigation instructions from 360-degree images captured on indoor routes.
The iWildCam 2021 Competition Dataset
The challenge is to classify species and count individual animals across sequences in the test cameras.
The surprising impact of mask-head architecture on novel class segmentation
Under this family, we study Mask R-CNN and discover that instead of its default strategy of training the mask-head with a combination of proposals and groundtruth boxes, training the mask-head with only groundtruth boxes dramatically improves its performance on novel classes.
A Closed-Form Learned Pooling for Deep Classification Networks
This operator can learn a strict super-set of what can be learned by average pooling or convolutions.
Straight to the point: reinforcement learning for user guidance in ultrasound
Point of care ultrasound (POCUS) consists in the use of ultrasound imaging in critical or emergency situations to support clinical decisions by healthcare professionals and first responders.
Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need
Large datasets have been crucial to the success of deep learning models in the recent years, which keep performing better as they are trained with more labelled data.
Unsupervised Learning of Disentangled Representations from Video
We present a new model DrNET that learns disentangled image representations from video.
A convolutional approach to reflection symmetry
We present a convolutional approach to reflection symmetry detection in 2D.
