2 citations · 2 across the 3 of their papers we have counts for
5 papers · 1 filter
Video BagNet: short temporal receptive fields increase robustness in long-term action recognition
Ombretta Strafforello, Xin Liu, Klamer Schutte +1
Previous work on long-term video action recognition relies on deep 3D-convolutional models that have a large temporal receptive field (RF). We argue that these models are not alway…
Objects do not disappear: Video object detection by single-frame object location anticipation
Xin Liu, Fatemeh Karimi Nejadasl, Jan C. van Gemert +2
Objects in videos are typically characterized by continuous smooth motion. We exploit continuous smooth motion in three ways. 1) Improved accuracy by using object motion as an addi…
No frame left behind: Full Video Action Recognition
Xin Liu, Silvia L. Pintea, Fatemeh Karimi Nejadasl +2
Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundre…
WeightAlign: Normalizing Activations by Weight Alignment
Xiangwei Shi, Yunqiang Li, Xin Liu +1
Batch normalization (BN) allows training very deep networks by normalizing activations by mini-batch sample statistics which renders BN unstable for small batch sizes. Current smal…
Cross Domain Image Matching in Presence of Outliers
Xin Liu, Seyran Khademi, Jan C. van Gemert
Cross domain image matching between image collections from different source and target domains is challenging in times of deep learning due to i) limited variation of image conditi…