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Found 13 papers, 5 papers with code
CRAFT: Contextual Re-Activation of Filters for face recognition Training
We show that CRAFT reduces fraction of inactive filters from 44% to 32% on average and discovers filter patterns not found by standard training.
LogicNet: A Logical Consistency Embedded Face Attribute Learning Network
We explore the potential reasons for this oversight and introduce two pressing challenges to the field: 1) How can we ensure that a model, when trained with data checked for logical consistency, yields predictions that are logically consistent?
Our Deep CNN Face Matchers Have Developed Achromatopsia
We show that such matchers achieve essentially the same accuracy on the grayscale or the color version of a set of test images.
Beard Segmentation and Recognition Bias
To investigate the effect of facial hair in a rigorous manner, we first created a set of fine-grained facial hair annotations to train a segmentation model and evaluate its accuracy across African-American and Caucasian face images.
What Should Be Balanced in a "Balanced" Face Recognition Dataset?
These datasets typically balance the number of identities and images across demographics.
Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning
We propose a logically consistent prediction loss, LCPLoss, to aid learning of logical consistency across attributes, and also a label compensation training strategy to eliminate the problem of no positive prediction across a set of related attributes.
Consistency and Accuracy of CelebA Attribute Values
Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency.
Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
This is the first work that we are aware of to explore how the level of brightness of the skin region in a pair of face images (rather than a single image) impacts face recognition accuracy, and to evaluate this as a systematic factor causing unequal accuracy across demographics.
Research on Fast Text Recognition Method for Financial Ticket Image
At present, a few works have applied deep learning methods to financial ticket recognition.
Towards Boosting the Channel Attention in Real Image Denoising : Sub-band Pyramid Attention
We equip the SPA blocks on a network designed for real image denoising.
Research on All-content Text Recognition Method for Financial Ticket Image
With the development of the economy, the number of financial tickets increases rapidly.
Financial ticket intelligent recognition system based on deep learning
Facing the rapid growth in the issuance of financial tickets (or bills, invoices etc.
Deep DIH : Statistically Inferred Reconstruction of Digital In-Line Holography by Deep Learning
Recently, end-to-end deep learning-based methods have been utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from a single-shot in-line digital hologram.
