Recent progress in semantic image segmentation
Semantic image segmentation, which becomes one of the key applications in image
processing and computer vision domain, has been used in multiple domains such as …
processing and computer vision domain, has been used in multiple domains such as …
Human pose estimation and its application to action recognition: A survey
Human pose estimation aims at predicting the poses of human body parts in images or
videos. Since pose motions are often driven by some specific human actions, knowing the …
videos. Since pose motions are often driven by some specific human actions, knowing the …
Reppoints: Point set representation for object detection
Modern object detectors rely heavily on rectangular bounding boxes, such as anchors,
proposals and the final predictions, to represent objects at various recognition stages. The …
proposals and the final predictions, to represent objects at various recognition stages. The …
The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
The Jaccard index, also referred to as the intersection-over-union score, is commonly
employed in the evaluation of image segmentation results given its perceptual qualities …
employed in the evaluation of image segmentation results given its perceptual qualities …
[PDF][PDF] SVPA-the segmentation based visual processing algorithm (SVPA) for illustration enhancements in digital video processing (DVP)
J Logeshwaran, M Ramkumar, T Kiruthiga… - ICTACT Journal on …, 2022 - ictactjournals.in
At the present time photographic visual processing is rapidly moving towards the next stage.
In addition, a variety of visual processing technologies are evolving, such as splitting image …
In addition, a variety of visual processing technologies are evolving, such as splitting image …
Hypercolumns for object segmentation and fine-grained localization
Recognition algorithms based on convolutional networks (CNNs) typically use the output of
the last layer as feature representation. However, the information in this layer may be too …
the last layer as feature representation. However, the information in this layer may be too …
Simultaneous detection and segmentation
We aim to detect all instances of a category in an image and, for each instance, mark the
pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS) …
pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS) …
Learning to see by moving
The current dominant paradigm for feature learning in computer vision relies on training
neural networks for the task of object recognition using millions of hand labelled images. Is it …
neural networks for the task of object recognition using millions of hand labelled images. Is it …
Learning non-maximum suppression
J Hosang, R Benenson… - Proceedings of the IEEE …, 2017 - openaccess.thecvf.com
Object detectors have hugely profited from moving towards an end-to-end learning
paradigm: proposals, fea tures, and the classifier becoming one neural network improved …
paradigm: proposals, fea tures, and the classifier becoming one neural network improved …
Towards understanding action recognition
Although action recognition in videos is widely studied, current methods often fail on real-
world datasets. Many recent approaches improve accuracy and robustness to cope with …
world datasets. Many recent approaches improve accuracy and robustness to cope with …