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Found 18 papers, 10 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.
Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language
Due to recent advances in pose-estimation methods, human motion can be extracted from a common video in the form of 3D skeleton sequences.
Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods
Generally, crowd datasets can be collected or generated from real or synthetic sources.
CrowdSim2: an Open Synthetic Benchmark for Object Detectors
Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in Computer Vision.
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.
A Spatio-Temporal Attentive Network for Video-Based Crowd Counting
Automatic people counting from images has recently drawn attention for urban monitoring in modern Smart Cities due to the ubiquity of surveillance camera networks.
ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval
In literature, this task is often used as a pre-training objective to forge architectures able to jointly deal with images and texts.
Ranked #22 on
Cross-Modal Retrieval
on COCO 2014
Transformer-Based Multi-modal Proposal and Re-Rank for Wikipedia Image-Caption Matching
With the increased accessibility of web and online encyclopedias, the amount of data to manage is constantly increasing.
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.
Generative Adversarial Networks for Astronomical Images Generation
Space exploration has always been a source of inspiration for humankind, and thanks to modern telescopes, it is now possible to observe celestial bodies far away from us.
AIMH at SemEval-2021 Task 6: Multimodal Classification Using an Ensemble of Transformer Models
This paper describes the system used by the AIMH Team to approach the SemEval Task 6.
Combining EfficientNet and Vision Transformers for Video Deepfake Detection
Traditionally, Convolutional Neural Networks (CNNs) have been used to perform video deepfake detection, with the best results obtained using methods based on EfficientNet B7.
Ranked #1 on
DeepFake Detection
on DFDC
(using extra training data)
Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features
It is designed for producing fixed-size 1024-d vectors describing whole images and sentences, as well as variable-length sets of 1024-d vectors describing the various building components of the two modalities (image regions and sentence words respectively).
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.
Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders
In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level.
Ranked #6 on
Image Retrieval
on Flickr30K 1K test
Transformer Reasoning Network for Image-Text Matching and Retrieval
State-of-the-art results in image-text matching are achieved by inter-playing image and text features from the two different processing pipelines, usually using mutual attention mechanisms.
Virtual to Real adaptation of Pedestrian Detectors
Furthermore, we demonstrate that with our Domain Adaptation techniques, we can reduce the Synthetic2Real Domain Shift, making closer the two domains and obtaining a performance improvement when testing the network over the real-world images.
