Search Results for author:
Found 13 papers, 6 papers with code
Is CLIP the main roadblock for fine-grained open-world perception?
Modern applications increasingly demand flexible computer vision models that adapt to novel concepts not encountered during training.
The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding
Recent advancements in large vision-language models enabled visual object detection in open-vocabulary scenarios, where object classes are defined in free-text formats during inference.
The Emotions of the Crowd: Learning Image Sentiment from Tweets via Cross-modal Distillation
Trends and opinion mining in social media increasingly focus on novel interactions involving visual media, like images and short videos, in addition to text.
Deep learning for structural health monitoring: An application to heritage structures
Thanks to recent advancements in numerical methods, computer power, and monitoring technology, seismic ambient noise provides precious information about the structural behavior of old buildings.
Recurrent Vision Transformer for Solving Visual Reasoning Problems
In the end, this study can lay the basis for a deeper understanding of the role of attention and recurrent connections for solving visual abstract reasoning tasks.
Multi-Camera Vehicle Counting Using Edge-AI
This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras.
Solving the Same-Different Task with Convolutional Neural Networks
With the experiments carried out in this work, we demonstrate that residual connections, and more generally the skip connections, seem to have only a marginal impact on the learning of the proposed problems.
Combining GANs and AutoEncoders for Efficient Anomaly Detection
In this work, we propose CBiGAN -- a novel method for anomaly detection in images, where a consistency constraint is introduced as a regularization term in both the encoder and decoder of a BiGAN.
The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval
In this paper, we describe in details VISIONE, a video search system that allows users to search for videos using textual keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity.
Automatic Pass Annotation from Soccer VideoStreams Based on Object Detection and LSTM
In this paper, we describe PassNet, a method to recognize the most frequent events in soccer, i. e., passes, from video streams.
Detection of Face Recognition Adversarial Attacks
In this frame, the contribution of our work is four-fold: i) we tested our recently proposed adversarial detection approach against classifier attacks, i. e. adversarial samples crafted to fool a FR neural network acting as a classifier; ii) using a k-Nearest Neighbor (kNN) algorithm as a guidance, we generated deep features attacks against a FR system based on a DL model acting as features extractor, followed by a kNN which gives back the query identity based on features similarity; iii) we used the deep features attacks to fool a FR system on the 1:1 Face Verification task and we showed their superior effectiveness with respect to classifier attacks in fooling such type of system; iv) we used the detectors trained on classifier attacks to detect deep features attacks, thus showing that such approach is generalizable to different types of offensives.
Exploring epoch-dependent stochastic residual networks
The recently proposed stochastic residual networks selectively activate or bypass the layers during training, based on independent stochastic choices, each of which following a probability distribution that is fixed in advance.
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation.
