Discriminant Saliency for Visual Recognition from Cluttered Scenes

By Dashan Gao, Nuno Vasconcelos. 2004

231 citations recorded by the source corpus.

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Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, background): “by highlighting locations that are (maximally) informative with respect to a discriminative task (Gao and Vasconcelos, 2005; Cao et al., 2015; Zhmoginov et al., 2019).”
Revisiting Video Saliency: A Large-Scale Benchmark and a New Model (2018, background): “Early static saliency models [36, 69, 15, 7, 18, 22, 33, 63] are mostly based on the contrast assumption that conspicuous visual features “pop-out” and involuntarily capture attention (see [5, 6] for review).”
Static and space-time visual saliency detection by self-resemblance. (2009, background): “Gao et al. (2008), Gao and Vasconcelos (2004), and Gao and Vasconcelos (2005) proposed a unified framework for top-down and bottom-up saliency as a classification problem with the objective being the minimization of classification error.”
WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians (2015, methodology): “Unlike general methods for saliency detection -with different degrees of bottom-up [34, 35, 36, 37, 38, 39] or top-down implementation [40, 41] - our approach appears especially tuned to the model of appearance associated for polyps [18].”
Region-Based Saliency Detection and Its Application in Object Recognition (2014, background): “Saliency detection has a potential application in object recognition [25], [58], [62].”
On the plausibility of the discriminant center-surround hypothesis for visual saliency. (2008, background): “Since the metric makes use of all saliency information in both the human fixations and the saliency detector output, it has been adopted in various recent studies (Bruce & Tsotsos, 2006; Harel et al., 2007; Kienzle et al., 2007). The predictions of discriminant saliency were compared to those of the”
A Benchmark of Computational Models of Saliency to Predict Human Fixations (2012, background): “Similarly, Gao and Vasconcelos and colleagues [22], [23] and [24] propose a unified model for top-down”
Learning Unsupervised Video Object Segmentation Through Visual Attention (2019, background): “Early attention models [42, 90, 19, 6, 22, 25, 36, 15, 20, 26, 61, 22] are based on biologically-inspired features (color, edge, optical ﬂow, etc .) and cognitive theories about visual attention (attention shift [39], feature integration theory [68], guided search [82], etc .).”
Deep Visual Attention Prediction (2017, background): “Since then, many follow-up works [5], [30] have been proposed in this direction.”
State-of-the-Art in Visual Attention Modeling (2013, background): “This is the case of discriminant saliency [146], [147], [215], but also appears in Harel et al.”
Revisiting Video Saliency Prediction in the Deep Learning Era (2021, background): “Early static saliency models [27], [45], [46], [47], [48], [49], [50], [51] are mostly concerned with the bottom-up visual attention mechanism (see [52] for detailed review).”
A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping (2019, unknown): “recognition [13], [20], object segmentation [10], [12], [14],”
Salient Object Detection: A Benchmark (2015, background): “Visual saliency is equated to discrimination in [62] and extended to bottom-up mechanism in the pre-attentive biological vision.”
SUN: A Bayesian framework for saliency using natural statistics. (2008, methodology): “Gao and Vasconcelos (2004, 2007) proposed a speciﬁc goal for saliency: classiﬁcation.”