Eye tracking assisted extraction of attentionally important objects from videos

By Shanmugavadivel Karthikeyan, Thuyen Ngo, Miguel P. Eckstein, Bangalore S. Manjunath. 2015

57 citations recorded by the source corpus.

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Citations in the corpus

A Comprehensive Survey of Deep Learning for Image Captioning (2018, background): “Thus, gaze mechanisms have already shown their potential performances in eye-based user modeling [18, 35, 109, 122, 124], object localization [100] or recognition [67], and holistic scene understanding [158, 160].”
What are the Visual Features Underlying Human Versus Machine Vision? (2017, background): “Researchers have found that in cases like these human perceptual judgments can augment the performance of vision models [35], resulting in significant gains in face recognition and localization [24, 2, 14], action recognition [31], object detection and segmentation [20, 26, 32].”
Seeing with Humans: Gaze-Assisted Neural Image Captioning (2016, methodology): “Consequently, gaze has successfully been used for tasks such as eye-based user modeling [1], [2], [3], [4], [5] and opened up new opportunities to further advance human-machine collaboration – collaborative human-machine vision systems in which part of the processing is carried out by the machine…”
Y Qualification Main task Y Return N N Y User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images (2017, methodology): “Such data have been used as a weak supervisory signal to localize objects on images [41, 45] or videos [57, 40].”
Shifting More Attention to Video Salient Object Detection (2019, background): “4* M&C++ 10 ETPM [64] 2015 CVPR eye tracking prior T N/A 11 RWRV [35] 2015 TIP random walk T 0.”
Recurrent Mixture Density Network for Spatiotemporal Visual Attention (2016, background): “, 2014), salient object detection (Li & Yu, 2015; Karthikeyan et al., 2015) and activity recognition (Vig et al.”