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Found 10 papers, 5 papers with code
Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition
In this paper, we explore the potential of the Contrastive Language-Image Pretraining (CLIP) model in scene text recognition (STR), and establish a novel Symmetrical Linguistic Feature Distillation framework (named CLIP-OCR) to leverage both visual and linguistic knowledge in CLIP.
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries.
Functional sufficient dimension reduction through information maximization with application to classification
Considering the case where the response variable is a categorical variable and the predictor is a random function, two novel functional sufficient dimensional reduction (FSDR) methods are proposed based on mutual information and square loss mutual information.
Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition
Vision model have gained increasing attention due to their simplicity and efficiency in Scene Text Recognition (STR) task.
Federated Covariate Shift Adaptation for Missing Target Output Values
The most recent multi-source covariate shift algorithm is an efficient hyperparameter optimization algorithm for missing target output.
Online Kernel Sliced Inverse Regression
Online dimension reduction is a common method for high-dimensional streaming data processing.
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Federated learning has become a popular tool in the big data era nowadays.
Exploring Stroke-Level Modifications for Scene Text Editing
Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are proposed to fill the blank of public evaluation datasets.
Complex Question Decomposition for Semantic Parsing
We conduct experiments on COMPLEXWEBQUESTIONS which is a large scale complex question semantic parsing dataset, results show that our model achieves significant improvement compared to state-of-the-art methods.
Ranked #1 on
Semantic Parsing
on complexWebQuestions-V1.0
Pretraining-Based Natural Language Generation for Text Summarization
For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence.
Ranked #30 on
Abstractive Text Summarization
on CNN / Daily Mail
