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Found 8 papers, 5 papers with code
VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
The third edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop featured four data-impaired challenges, focusing on addressing the limitations of data availability in training deep learning models for computer vision tasks.
Evaluating Context for Deep Object Detectors
Which object detector is suitable for your context sensitive task?
VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
The second edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges featured five data-impaired challenges, where models are trained from scratch on a reduced number of training samples for various key computer vision tasks.
Hallucination In Object Detection -- A Study In Visual Part Verification
We show that object detectors can hallucinate and detect missing objects; potentially even accurately localized at their expected, but non-existing, position.
VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges.
t-EVA: Time-Efficient t-SNE Video Annotation
Video understanding has received more attention in the past few years due to the availability of several large-scale video datasets.
Tilting at windmills: Data augmentation for deep pose estimation does not help with occlusions
Occlusion degrades the performance of human pose estimation.
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant.
