Search Results for author: Feiyan Hu

Found 8 papers, 3 papers with code

Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach

no code implementations27 Nov 2023 Ayush K. Rai, Tarun Krishna, Feiyan Hu, Alexandru Drimbarean, Kevin McGuinness, Alan F. Smeaton, Noel E. O'Connor

Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos with normal instances while test data contains both normal and anomalous instances.

One-Class Classification Open Set Learning +2

Improving Person Re-Identification with Temporal Constraints

no code implementations17 Nov 2021 Julia Dietlmeier, Feiyan Hu, Frances Ryan, Noel E. O'Connor, Kevin McGuinness

We apply state-of-the-art person re-identification models to our dataset and show that by leveraging the available timestamp information we are able to achieve a significant gain of 37. 43% in mAP and a gain of 30. 22% in Rank1 accuracy.

Person Re-Identification Re-Ranking

Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates

no code implementations18 Dec 2020 Feiyan Hu, Eva Mohedano, Noel O'Connor, Kevin McGuinness

Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources.

Classification General Classification +1

FastSal: a Computationally Efficient Network for Visual Saliency Prediction

1 code implementation25 Aug 2020 Feiyan Hu, Kevin McGuinness

This paper focuses on the problem of visual saliency prediction, predicting regions of an image that tend to attract human visual attention, under a constrained computational budget.

Saliency Prediction Transfer Learning

Utilising Visual Attention Cues for Vehicle Detection and Tracking

no code implementations31 Jul 2020 Feiyan Hu, Venkatesh G M, Noel E. O'Connor, Alan F. Smeaton, Suzanne Little

We investigate: 1) How a visual attention map such as a \emph{subjectness} attention or saliency map and an \emph{objectness} attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects.

Object object-detection +2

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