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Found 15 papers, 4 papers with code
TextAug: Test time Text Augmentation for Multimodal Person Re-identification
In this study, we investigate the effectiveness of two computer vision data augmentation techniques: cutout and cutmix, for text augmentation in multi-modal person re-identification.
Data Augmentation through Pseudolabels in Automatic Region Based Coronary Artery Segmentation for Disease Diagnosis
Coronary Artery Diseases(CADs) though preventable are one of the leading causes of death and disability.
ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis
Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips.
Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation
We employed our method on two powerful and widely used semantic segmentation networks: UNet and U2Net to train in a multi-task setup.
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition
This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results.
Filtering Point Targets via Online Learning of Motion Models
Filtering point targets in highly cluttered and noisy data frames can be very challenging, especially for complex target motions.
Real-time tracker with fast recovery from target loss
When the confidence is low, we avoid updating the object's position through the feature map; instead, the tracker passes to a single-frame failure mode, during which the patch's low-level visual content is used to swiftly update the object's position, before recovering from the target loss in the next frame.
Instance Retrieval at Fine-grained Level Using Multi-Attribute Recognition
In this paper, we present a method for instance ranking and retrieval at fine-grained level based on the global features extracted from a multi-attribute recognition model which is not dependent on landmarks information or part-based annotations.
Convolutional Recurrent Predictor: Implicit Representation for Multi-target Filtering and Tracking
Defining a multi-target motion model, which is an important step of tracking algorithms, can be very challenging.
Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition
After selecting categories with sufficient number of images for training, we remove very scarce attributes and merge the duplicate ones in each category, then we clean the dataset based on the new list of attributes.
Hide and Seek tracker: Real-time recovery from target loss
In the case of a low confidence, the position update is rejected and the tracker passes to a single-frame failure mode, during which the patch low-level visual content is used to swiftly update the object position, before recovering from the target loss in the next frame.
FineTag: Multi-attribute Classification at Fine-grained Level in Images
In this paper, we address the extraction of the fine-grained attributes of an instance as a `multi-attribute classification' problem.
Deep Recurrent Neural Network for Multi-target Filtering
This paper addresses the problem of fixed motion and measurement models for multi-target filtering using an adaptive learning framework.
