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Found 20 papers, 5 papers with code
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks
(ii) Some datasets that are assumed to be biased toward dynamics are actually biased toward static information.
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information
To show the efficacy of our approach, we analyse two widely studied tasks, action recognition and video object segmentation.
Simpler Does It: Generating Semantic Labels with Objectness Guidance
Extensive experiments demonstrate the high quality of our generated pseudo-labels and effectiveness of the proposed framework in a variety of domains.
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness
The premise is based on the notion of feature binding, which is defined as the process by which activations spread across space and layers in the network are successfully integrated to arrive at a correct inference decision.
Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs
In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via global pooling removes all spatial information.
Position, Padding and Predictions: A Deeper Look at Position Information in CNNs
; (ii) Does position encoding affect the learning of semantic representations?
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness
In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network.
Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve
Our experiment shows FN-AUC can measure spatial biases, central and peripheral, more effectively than S-AUC without penalizing the fixation locations.
How Much Position Information Do Convolutional Neural Networks Encode?
In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent.
Distributed Iterative Gating Networks for Semantic Segmentation
In this paper, we present a canonical structure for controlling information flow in neural networks with an efficient feedback routing mechanism based on a strategy of Distributed Iterative Gating (DIGNet).
Richer and Deeper Supervision Network for Salient Object Detection
Recent Salient Object Detection (SOD) systems are mostly based on Convolutional Neural Networks (CNNs).
SMILER: Saliency Model Implementation Library for Experimental Research
The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models.
Recurrent Iterative Gating Networks for Semantic Segmentation
The iterative nature of this mechanism allows for gating to spread in both spatial extent and feature space.
Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks
Further to this, we present data, analysis and baseline benchmark results towards addressing the problem of salient object ranking.
Semantics Meet Saliency: Exploring Domain Affinity and Models for Dual-Task Prediction
With that said, there is an apparent relationship between these two problem domains in that the composition of a scene and associated semantic categories is certain to play into what is deemed salient.
Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling
In order to address this issue, we also propose Gated Feedback Refinement Network (G-FRNet) that addresses this limitation.
EML-NET:An Expandable Multi-Layer NETwork for Saliency Prediction
Furthermore, the encoder can contain more than one CNN model to extract features, and models can have different architectures or be pre-trained on different datasets.
Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects
In this paper, we argue that work to date has addressed a problem that is relatively ill-posed.
Gated Feedback Refinement Network for Dense Image Labeling
Effective integration of local and global contextual information is crucial for dense labeling problems.
A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond
In this paper we consider the problem of visual saliency modeling, including both human gaze prediction and salient object segmentation.
