Search Results for author:
Found 27 papers, 10 papers with code
Following the Human Thread in Social Navigation
We propose the first Social Dynamics Adaptation model (SDA) based on the robot's state-action history to infer the social dynamics.
Weakly-Supervised Visual-Textual Grounding with Semantic Prior Refinement
The first untrained module aims to return a rough alignment between textual phrases and bounding boxes.
Distilling Knowledge for Short-to-Long Term Trajectory Prediction
Long-term trajectory forecasting is an important and challenging problem in the fields of computer vision, machine learning, and robotics.
Exploiting Proximity-Aware Tasks for Embodied Social Navigation
In this paper, we propose an end-to-end architecture that exploits Proximity-Aware Tasks (referred as to Risk and Proximity Compass) to inject into a reinforcement learning navigation policy the ability to infer common-sense social behaviors.
TAMFormer: Multi-Modal Transformer with Learned Attention Mask for Early Intent Prediction
Human intention prediction is a growing area of research where an activity in a video has to be anticipated by a vision-based system.
Where are my Neighbors? Exploiting Patches Relations in Self-Supervised Vision Transformer
Vision Transformers (ViTs) enabled the use of the transformer architecture on vision tasks showing impressive performances when trained on big datasets.
Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction
Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications.
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting
To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones).
Online Learning of Reusable Abstract Models for Object Goal Navigation
In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task.
SlowFast Rolling-Unrolling LSTMs for Action Anticipation in Egocentric Videos
Action anticipation in egocentric videos is a difficult task due to the inherently multi-modal nature of human actions.
Conditional Variational Capsule Network for Open Set Recognition
In this way, we are able to control the compactness of the features of the same class around the center of the gaussians, thus controlling the ability of the classifier in detecting samples from unknown classes.
Prediction of Tuberculosis using U-Net and segmentation techniques
One of the most serious public health problems in Peru and worldwide is Tuberculosis (TB), which is produced by a bacterium known as Mycobacterium tuberculosis.
Exploiting Scene-specific Features for Object Goal Navigation
We study this question in the context of Object Navigation, a problem in which an agent has to reach an object of a specific class while moving in a complex domestic environment.
Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images
Tuberculosis (TB), caused by a germ called Mycobacterium tuberculosis, is one of the most serious public health problems in Peru and the world.
Using Capsule Neural Network to predict Tuberculosis in lens-free microscopic images
Tuberculosis, caused by a bacteria called Mycobacterium tuberculosis, is one of the most serious public health problems worldwide.
AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction
Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications.
Knowledge Distillation for Action Anticipation via Label Smoothing
Since multiple actions may equally occur in the future, we treat action anticipation as a multi-label problem with missing labels extending the concept of label smoothing.
A CNN-RNN Framework for Image Annotation from Visual Cues and Social Network Metadata
Images represent a commonly used form of visual communication among people.
Social and Scene-Aware Trajectory Prediction in Crowded Spaces
Mimicking human ability to forecast future positions or interpret complex interactions in urban scenarios, such as streets, shopping malls or squares, is essential to develop socially compliant robots or self-driving cars.
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition
One of the main problems in webly-supervised learning is cleaning the noisy labeled data from the web.
Context-Aware Trajectory Prediction
To this end, we propose a "context-aware" recurrent neural network LSTM model, which can learn and predict human motion in crowded spaces such as a sidewalk, a museum or a shopping mall.
Am I Done? Predicting Action Progress in Videos
In this paper we deal with the problem of predicting action progress in videos.
Automatic Image Annotation via Label Transfer in the Semantic Space
Automatic image annotation is among the fundamental problems in computer vision and pattern recognition, and it is becoming increasingly important in order to develop algorithms that are able to search and browse large-scale image collections.
Knowledge Transfer for Scene-specific Motion Prediction
When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future.
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata
Some images that are difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata.
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image.
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing.
