Search Results for author:
Found 8 papers, 2 papers with code
Pair-wise Layer Attention with Spatial Masking for Video Prediction
To this end, we present a Pair-wise Layer Attention with Spatial Masking (PLA-SM) framework for video prediction to capture the spatiotemporal dynamics, which reflect the motion trend.
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery
Particularly, the gradients from the segmentation model are exploited to discover the easily confused region, in which it is difficult to identify the pixel-wise objects from the background in a frame.
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation
To alleviate the expensive human labeling, semi-supervised semantic segmentation employs a few labeled images and an abundant of unlabeled images to predict the pixel-level label map with the same size.
Fully Transformer-Equipped Architecture for End-to-End Referring Video Object Segmentation
Referring Video Object Segmentation (RVOS) requires segmenting the object in video referred by a natural language query.
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge.
Semantic Segmentation
Unsupervised Video Object Segmentation
+1
Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation
Specifically, we employ an asymmetric encoder to learn the compensating features of the RGB and the thermal images.
Ranked #17 on
Thermal Image Segmentation
on MFN Dataset
Fast Fourier Inception Networks for Occluded Video Prediction
Video prediction is a pixel-level task that generates future frames by employing the historical frames.
Exploring global diverse attention via pairwise temporal relation for video summarization
In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames.
