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Found 10 papers, 5 papers with code
Data Distribution Distilled Generative Model for Generalized Zero-Shot Recognition
To counter this, we introduce an end-to-end generative GZSL framework called D$^3$GZSL.
Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition
A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing, and can be flexibly plugged into the image classification network as a pre-network to exploit resolution and scale information.
PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress Classification
To overcome this drawback, we present a \textit{Patch Refiner} to cluster patches into different groups and only select the highest distress-risk group to yield a slim head for the final image classification.
Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels.
Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild
Specifically, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches to fully exploit the resolution and scale information.
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection
We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes.
Towards using social media to identify individuals at risk for preventable chronic illness
The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter.
Sparse Graph-based Transduction for Image Classification
Then the Laplacian eigenmapping is adopted for deriving the graph Laplacian of the graph.
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
We further exploit the representational power of Haar wavelet and present a novel low-level face representation named Shape Primitives Histogram (SPH) for face recognition.
