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Found 9 papers, 8 papers with code
Memory-augmented Online Video Anomaly Detection
The ability to understand the surrounding scene is of paramount importance for Autonomous Vehicles (AVs).
Improving Localization for Semi-Supervised Object Detection
Nowadays, Semi-Supervised Object Detection (SSOD) is a hot topic, since, while it is rather easy to collect images for creating a new dataset, labeling them is still an expensive and time-consuming task.
LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation
The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control.
AEDA: An Easier Data Augmentation Technique for Text Classification
This is an easier technique to implement for data augmentation than EDA method (Wei and Zou, 2019) with which we compare our results.
Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing
Within the field of instance segmentation, most of the state-of-the-art deep learning networks rely nowadays on cascade architectures, where multiple object detectors are trained sequentially, re-sampling the ground truth at each step.
Ranked #1 on
Instance Segmentation
on coco minval
UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model
We tackle this problem utilizing a combination of a state-of-the-art pre-trained language model (CharacterBERT) and a traditional bag-of-words technique.
Improving BERT Performance for Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products.
Ranked #3 on
Aspect Extraction
on SemEval 2014 Task 4 Sub Task 2
A novel Region of Interest Extraction Layer for Instance Segmentation
Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible.
Ranked #66 on
Instance Segmentation
on COCO minival
Adversarial Training for Aspect-Based Sentiment Analysis with BERT
In this work, we apply adversarial training, which was put forward by Goodfellow et al. (2014), to the post-trained BERT (BERT-PT) language model proposed by Xu et al. (2019) on the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis.
Ranked #4 on
Aspect Extraction
on SemEval 2014 Task 4 Sub Task 2
