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Found 25 papers, 11 papers with code
Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator, R$^2$T is tailored to re-embed instance features online.
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.
Gaussian-based Probabilistic Deep Supervision Network for Noise-Resistant QoS Prediction
Quality of Service (QoS) prediction is an essential task in recommendation systems, where accurately predicting unknown QoS values can improve user satisfaction.
Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification
Moreover, the student is used to update the teacher with an exponential moving average (EMA), which in turn identifies new hard instances for subsequent training iterations and stabilizes the optimization.
Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection
DKE employs a segmentation module to segment the shrunken text region as the text kernel, then expands the text kernel contour to obtain text boundary by regressing the vertex-wise offsets.
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.
Deep Domain Adaptation for Pavement Crack Detection
In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available.
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning
DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner.
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution.
Deep Semantic Dictionary Learning for Multi-label Image Classification
Compared with single-label image classification, multi-label image classification is more practical and challenging.
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.
CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCA
This paper describes the systems of the CUNY-PKU team in SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA.
Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis
We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser.
Quasi-Second-Order Parsing for 1-Endpoint-Crossing, Pagenumber-2 Graphs
We propose a new Maximum Subgraph algorithm for first-order parsing to 1-endpoint-crossing, pagenumber-2 graphs.
Parsing to 1-Endpoint-Crossing, Pagenumber-2 Graphs
We study the Maximum Subgraph problem in deep dependency parsing.
Regression-based Hypergraph Learning for Image Clustering and Classification
Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph Transduction (RHT) models for addressing the image clustering and classification issues.
Learning Hypergraph-regularized Attribute Predictors
Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space.
On The Effect of Hyperedge Weights On Hypergraph Learning
However, many studies on pairwise graphs show that the choice of edge weight can significantly influence the performances of such graph algorithms.
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.
Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition
In our algorithm, the discriminating power of DLPP are further exploited from two aspects.
Face Recognition via Globality-Locality Preserving Projections
We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structures of data.
