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Found 9 papers, 8 papers with code
Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals
Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global categories within an image corpus without any form of annotation.
Benchmarking Video Frame Interpolation
Video frame interpolation, the task of synthesizing new frames in between two or more given ones, is becoming an increasingly popular research target.
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods
Using our tools, we report results for 24 different combinations of neural models and XAI methods, demonstrating the strengths and weaknesses of the assessed methods in a fully automatic and systematic manner.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Content-Adaptive Downsampling in Convolutional Neural Networks
Many convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network's receptive field and decrease computational cost.
Efficient Feature Extraction for High-resolution Video Frame Interpolation
The feature extraction layers help to compress the input and extract relevant information for the latter stages, such as motion estimation.
$S^2$-Flow: Joint Semantic and Style Editing of Facial Images
In addition, methods that use semantic masks to edit images have difficulty preserving the identity and are unable to perform controlled style edits.
Entropy-driven Unsupervised Keypoint Representation Learning in Videos
Two original information-theoretic losses, computed from local entropy, guide our model to discover consistent keypoint representations; a loss that maximizes the spatial information covered by the keypoints and a loss that optimizes the keypoints' information transportation over time.
Fast Axiomatic Attribution for Neural Networks
Mitigating the dependence on spurious correlations present in the training dataset is a quickly emerging and important topic of deep learning.
Dense Unsupervised Learning for Video Segmentation
On established VOS benchmarks, our approach exceeds the segmentation accuracy of previous work despite using significantly less training data and compute power.
