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Works193 from public data
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- Enhanced-alignment Measure for Binary Foreground Map Evaluation1,716
This paper takes a detailed look at current binary FM evaluation measures and proposes a novel and effective E-measure (Enhanced-alignment measure), jointly capturing image-level statistics and local pixel matching information, which demonstrates the superiority of this measure over the available measures on 4 popular datasets via 5 meta-measures.
- Deeply Supervised Salient Object Detection with Short Connections1,458
This paper proposes a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture, which takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection.
- Pros and cons of GAN evaluation measures1,003
More than 19 quantitative and 4 qualitative measures for evaluating generative models with a particular emphasis on GAN-derived models are reviewed and critically discussed.
- 1Salient Object Detection: A Survey848
A comprehensive review of recent progress in salient object detection is provided and this field is situate among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction.
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- Salient Object Detection: A Benchmark416
This work analyzes benchmark datasets and scoring techniques and provides a quantitative comparison of 35 state-of-the-art saliency detection models to identify the scenes where models or humans fail to detect the most salient object.
- BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network402
This paper proposes a bifurcated backbone strategy (BBS) to split the multi-level features of RGB-D salient object detection into teacher and student features, and utilizes a depth-enhanced module (DEM) to excavate informative parts of depth cues from the channel and spatial views.
- Pros and cons of GAN evaluation measures: New developments393
New dimensions that are becoming important in assessing models (e.g. bias and fairness) are described and the connection between GAN evaluation and deepfakes is discussed, which are important areas of concern in the machine learning community today and progress in GAN Evaluation can help mitigate them.
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- Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground347
This work identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter, and proposes a new high quality dataset and updates the previous saliency benchmark.
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- Revisiting Video Saliency: A Large-Scale Benchmark and a New Model311
This work introduces a new benchmark for predicting human eye movements during dynamic scene free-viewing, and proposes a novel video saliency model that augments the CNN-LSTM network architecture with an attention mechanism to enable fast, end-to-end saliency learning.
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- Defending Yarbus: Eye movements reveal observers' task267
Yarbus's idea that human eye-movement patterns are modulated top down by different task demands is supported by the data and continues to be an inspiration for future computational and experimental eye- Movement research.
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- Cross-View Image Synthesis Using Conditional GANs231
This paper attempts to solve the novel problem of cross-view image synthesis, aerial to street-view and vice versa, using conditional generative adversarial networks (cGAN), and proposes two new architectures called Crossview Fork (X-Fork) and Crossview Sequential (x-Seq).
- A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'217
The effects of fitting a distribution or a model to the data, followed by repeated sampling from it, and the effects of fitting a distribution or a model to the data, followed by repeated sampling from it are investigated.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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