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Found 13 papers, 5 papers with code
Adversarial Machine Learning-Enabled Anonymization of OpenWiFi Data
Data privacy and protection through anonymization is a critical issue for network operators or data owners before it is forwarded for other possible use of data.
Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective
We further show that when no pretraining is done or when the pretrained transformer models are used with non-natural images (e. g. medical data), CNNs tend to generalize better than transformers at even very small coreset sizes.
Ranked #3 on
Image Classification
on Tiny ImageNet Classification
Under the Hood of Transformer Networks for Trajectory Forecasting
This paper proposes the first in-depth study of Transformer Networks (TF) and Bidirectional Transformers (BERT) for the forecasting of the individual motion of people, without bells and whistles.
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
As for the data, we show that the autonomous driving benchmarks are monotonous in nature, that is, they are not diverse in scenarios and dense in pedestrians.
An integrated light management system with real-time light measurement and human perception
Illumination is important for well-being, productivity and safety across several environments, including offices, retail shops and industrial warehouses.
Generalizable Pedestrian Detection: The Elephant In The Room
Furthermore, we illustrate that diverse and dense datasets, collected by crawling the web, serve to be an efficient source of pre-training for pedestrian detection.
Ranked #3 on
Pedestrian Detection
on CityPersons
(using extra training data)
Transformer Networks for Trajectory Forecasting
In particular, the TF model without bells and whistles yields the best score on the largest and most challenging trajectory forecasting benchmark of TrajNet.
Ranked #12 on
Trajectory Prediction
on ETH/UCY
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited.
Ranked #8 on
Pedestrian Detection
on Caltech
(using extra training data)
Human-centric light sensing and estimation from RGBD images: The invisible light switch
ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person).
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
In this work, we explore the correlation between people trajectories and their head orientations.
RGBD2lux: Dense light intensity estimation with an RGBD sensor
The proposed method uses both depth data and images from the sensor to provide a dense measure of light intensity in the field of view of the camera.
MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures.
“Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention
In this paper we show the importance of the head pose estimation in the task of trajectory forecasting.
