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Found 8 papers, 3 papers with code
Morphable Detector for Object Detection on Demand
Once an MD is learned, it is able to use a few samples of a novel class to directly compute its prototype to fulfill the online morphing process.
Contrastive Learning for Label Efficient Semantic Segmentation
While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases.
Contrastive Learning for Label-Efficient Semantic Segmentation
While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases.
Object Detection with a Unified Label Space from Multiple Datasets
To address this challenge, we design a framework which works with such partial annotations, and we exploit a pseudo labeling approach that we adapt for our specific case.
Recognizing Part Attributes with Insufficient Data
Although great progress has been made to apply object-level recognition, recognizing the attributes of parts remains less applicable since the training data for part attributes recognition is usually scarce especially for internet-scale applications.
Pseudo Mask Augmented Object Detection
In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box annotation.
Peak-Piloted Deep Network for Facial Expression Recognition
Objective functions for training of deep networks for face-related recognition tasks, such as facial expression recognition (FER), usually consider each sample independently.
Ranked #2 on
Facial Expression Recognition (FER)
on Oulu-CASIA
