A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Grid Saliency for Context Explanations of Semantic Segmentation
[article]
2019
arXiv
pre-print
Preserved Fulltext
Other Versions
As the proposed grid saliency allows to spatially disentangle the object and its context, we specifically explore its potential to produce context explanations for semantic segmentation networks, discovering ...
Our results show that grid saliency can be successfully used to provide easily interpretable context explanations and, moreover, can be employed for detecting and localizing contextual biases present in ...
We showcase the usability of grid saliency for context explanations of semantic segmentation (see Sec. 4 and 5). ...
arXiv:1907.13054v2
fatcat:ik7vn7rqwrbi3irkfmm6gnnkhu
SegNBDT: Visual Decision Rules for Segmentation
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We obtain semantic visual meaning by extending saliency methods to segmentation and attain accuracy by leveraging insights from neural-backed decision trees, a deep learning analog of decision trees for ...
However, such models (1) perform poorly when compared to state-of-the-art segmentation models or (2) fail to produce decision rules with spatially-grounded semantic meaning. ...
We furthermore propose extensions for saliency methods -the spatially-aware Grad-PAM and semantically-aware SIR -to uncover semantic, visual decision rules in our neural-backed decision tree for segmentation ...
arXiv:2006.06868v1
fatcat:4if6moi6nje3pbckhp2r3oxqwq
Salience and metaphysical explanation
2021
Synthese
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
I argue that such explanations exhibit salience failure. How ought we represent the semantics of salience? ...
of focus sensitivity to sketch how one might model the role of salience in these kinds of explanations. ...
But analogous semantic differences may be induced by the context of the explanation. ...
doi:10.1007/s11229-021-03267-5
fatcat:re3e7ddok5aivikofzjckbdf64
Neural Image Compression and Explanation
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Extensive experiments across multiple image classification benchmarks demonstrate the superior performance of NICE compared to the state-of-the-art methods in terms of explanation quality and semantic ...
compress the input images for efficient storage or transmission. ...
The authors would also gratefully acknowledge the support of VMware Inc. for its university research fund to this research. ...
arXiv:1908.08988v2
fatcat:lanra55aafg3hmzym3r36trtfa
Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We address the problem of weakly-supervised semantic segmentation (WSSS) using bounding box annotations. ...
This allows to extract high-quality pseudo segmentation labels to train CNNs for semantic segmentation, but the labels still contain noise especially at object boundaries. ...
Classifier for semantic segmentation Here we provide a detailed description of two different classifiers for semantic segmentation. ...
arXiv:2104.00905v1
fatcat:ibtxkeqehvg77h3r4yc4kkp4ta
Neural Image Compression and Explanation
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The authors would also gratefully acknowledge the support of VMware Inc. for its university research fund to this research. ...
The authors would like to thank the anonymous reviewers for their comments and suggestions, which helped improve the quality of this paper. ...
The sparse mask generator of NICE is also related to a large body of research on semantic segmentation [26] - [32] . ...
doi:10.1109/access.2020.3041416
fatcat:qw3ow3wirnddnhsbralzcxcvcy
Self-explanatory Deep Salient Object Detection
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Extensive experiments on five popular benchmark datasets and the visualized saliency explanation demonstrate that the proposed method provides new state-of-the-art. ...
More specifically, we develop a multi-stage saliency encoder to extract multi-scale features which contain both low- and high-level saliency context. ...
[22] performed a multi-task learning scheme in conjunction with the task of semantic segmentation. ...
arXiv:1708.05595v1
fatcat:7wyalxf7drb5tgsqm62noaqyh4
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. ...
Under the fully unsupervised setting, there is no precedent in solving the semantic segmentation task on such a challenging benchmark. ...
The combination of these two properties results in image representations that can be directly clustered into semantic groups (see also [73] for a more detailed explanation). ...
arXiv:2102.06191v3
fatcat:6kbodv6wbjblzorfimrs4hz5v4
Explainability of deep vision-based autonomous driving systems: Review and challenges
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The concept of explainability has several facets and the need for explainability is strong in driving, a safety-critical application. ...
First, it discusses definitions, context, and motivation for gaining more interpretability and explainability from self-driving systems, as well as the challenges that are specific to this application. ...
In (Caltagirone et al, 2017) , the network is trained to predict the track of the future positions of the vehicle, in a semantic segmentation fashion. ...
arXiv:2101.05307v2
fatcat:c4y7wkesfrczpiw3v6eywdh5m4
Survival processing modulates the neurocognitive mechanisms of episodic encoding
2020
Cognitive, Affective, & Behavioral Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The results are consistent with a richness of encoding account of the survival processing effect and offer novel insights into the encoding processes that lead to enhanced memory for fitness-relevant information ...
Memories formed in the context of an imagined survival scenario are more easily remembered, but the mechanisms underlying this effect are still under debate. ...
Thus, one explanation for the decreased false memory in the present study could be that the strength of semantic associations within our word list was in fact relatively low. ...
doi:10.3758/s13415-020-00798-1
pmid:32430899
fatcat:htsq24rk2bdltloyb74jbror34
Video segmentation by tracing discontinuities in a trajectory embedding
2012
2012 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
For segmenting articulated objects, we combine motion grouping cues with a centersurround saliency operation, resulting in "context-aware", spatially coherent, saliency maps. ...
Detected discontinuities are strong indicators of object boundaries and thus valuable for video segmentation. ...
Acknowledgments The authors would like to thank Kosta Derpanis, Elena Bernardis, Weiyu Zhang and Ben Sapp for useful discussions on the writing of this paper. ...
doi:10.1109/cvpr.2012.6247883
dblp:conf/cvpr/FragkiadakiZS12
fatcat:lic7g6p5wbfzdanzgfwbwjyp2u
Landmarks in wayfinding: a review of the existing literature
2021
Cognitive Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
visibility and salience. ...
However, visibility of landmarks as well as visual and cognitive saliency need to be further investigated using different environments, tasks or different levels of familiarity with environments. ...
For the final component, contextual saliency, they focused on two types of contexts: task-based context (which includes the types of tasks) and modality-based context, which includes the mode of transportation ...
doi:10.1007/s10339-021-01012-x
pmid:33682034
fatcat:rh7rldrgsvgcpgxwqfmyuk6xwu
Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps
[article]
2021
biorxiv/medrxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We tested this explanation using contextualized meaning maps, a method that is based on crowd-sourced ratings to quantify the spatial distribution of context-sensitive 'meaning' in images. ...
In summary, we demonstrated that - in the context of our rating task - semantically inconsistent objects are experienced as less meaningful than their consistent counterparts, and that contextualized meaning ...
Acknowledgments We would like to thank Antje Nuthmann and Tom Freeman for their comments on an earlier ...
doi:10.1101/2021.05.03.442533
fatcat:a2xwrmaz7vfm3etbfqx53aps6q
Meaning guides attention in real-world scene images: Evidence from eye movements and meaning maps
2018
Journal of Vision
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
, meaning accounted for unique variance in attention whereas salience did not. ...
Meaning was captured by "meaning maps" representing the spatial distribution of semantic information in scenes. ...
How can we account for the results of these earlier studies? One explanation can be found in the strong correlation between meaning and visual salience. ...
doi:10.1167/18.6.10
pmid:30029216
pmcid:PMC6012218
fatcat:2ickstynvzgvvk6tpmnf5dx2da
A Brief Survey on Weakly Supervised Semantic Segmentation
2022
International Journal of Online and Biomedical Engineering (iJOE)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Semantic Segmentation is the process of assigning a label to every pixel in the image that share same semantic properties and stays a challenging task in computer vision. ...
In recent years, and due to the large availability of training data the performance of semantic segmentation has been greatly improved by using deep learning techniques. ...
The list below is a non-exhaustive example of datasets for images semantic segmentation. ...
doi:10.3991/ijoe.v18i10.31531
fatcat:6klflaiecrdgrizzlpgybimt6q
« Previous
Showing results 1 — 15 out of 1,562 results