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Found 13 papers, 9 papers with code
PersonRank: Detecting Important People in Images
Always, some individuals in images are more important/attractive than others in some events such as presentation, basketball game or speech.
One-pass Person Re-identification by Sketch Online Discriminant Analysis
While there is a few work on discussing online re-id, most of them require considerable storage of all passed data samples that have been ever observed, and this could be unrealistic for processing data from a large camera network.
Learning to Learn Relation for Important People Detection in Still Images
In this work, we propose a deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling and feature learning.
Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks
Visible watermark plays an important role in image copyright protection and the robustness of a visible watermark to an attack is shown to be essential.
Learning to Impute: A General Framework for Semi-supervised Learning
Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies.
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection
Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern.
Knowledge Distillation for Multi-task Learning
We then learn the multi-task model for minimizing task-specific loss and for producing the same feature with task-specific models.
MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection
We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but without expensive supervision of manually annotating highlight segments.
Universal Representation Learning from Multiple Domains for Few-shot Classification
In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples.
Ranked #4 on
Few-Shot Image Classification
on Meta-Dataset
Cross-domain Few-shot Learning with Task-specific Adapters
In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples.
Ranked #2 on
Few-Shot Image Classification
on Meta-Dataset
cross-domain few-shot learning
Few-Shot Image Classification
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets.
Universal Representations: A Unified Look at Multiple Task and Domain Learning
We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network.
Multi-task Learning with 3D-Aware Regularization
Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.
