Search Results for author:
Found 22 papers, 11 papers with code
Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts
In this paper, we present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA).
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.
Transferring Knowledge with Attention Distillation for Multi-Domain Image-to-Image Translation
Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks.
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
MetalGAN: Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning
Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation.
MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial Colorization
In recent years, the majority of works on deep-learning-based image colorization have focused on how to make a good use of the enormous datasets currently available.
Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval
We propose to use genetic algorithms to find the optimal setting of all the diffusion parameters with respect to retrieval performance for each different dataset.
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance.
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval
The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks.
An accurate retrieval through R-MAC+ descriptors for landmark recognition
The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent results have been obtained.
Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
It allows to drastically reduce the query time and outperforms the accuracy results compared to the state-of-the-art methods for large-scale landmark recognition.
A complete hand-drawn sketch vectorization framework
Vectorizing hand-drawn sketches is a challenging task, which is of paramount importance for creating CAD vectorized versions for the fashion and creative workflows.
Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets
In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed.
A location-aware embedding technique for accurate landmark recognition
The current state of the research in landmark recognition highlights the good accuracy which can be achieved by embedding techniques, such as Fisher vector and VLAD.
Large-scale Image Geo-Localization Using Dominant Sets
We cast the geo-localization as a clustering problem on local image features.
