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Transcription-Enriched Joint Embeddings or Spoken Descriptions of Images and Videos. In CVPR 2020 Workshop on Egocentric Perception, Interaction and Computing. Seattle, WA, USA: arXiv; 2020. (96.79 KB)
. . Multimodal Hate Speech Detection in Memes. . 2019. (1.66 MB)
. Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation. In NeurIPS 2019 Workshop on AI for Social Good. Vancouver, Canada; 2019. (1.91 MB)
. SurvLIMEpy: A Python package implementing SurvLIME. Expert Systems With Applications. 2024;237, Part C.
. Precision-Recall-Classification Evaluation Framework: Application to Depth Estimation on Single Images. In European Conference on Computer Vision (ECCV). Zurich; 2014. (1.37 MB)
. Monocular Depth Ordering Using T-junctions and Convexity Occlusion Cues. IEEE Transactions on Image Processing. 2013;22(5): 1926 - 1939 . (2.64 MB)
. Depth Ordering on Image Sequences Using Motion Occlusions. In IEEE Int. Conf. in Image Processing, ICIP 2012. Orlando, Florida, USA; 2012. (5.42 MB)
. Occlusion-based depth ordering on monocular images with binary partition tree. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011. Prague, Czech Republic; 2011. pp. 1093–1096. (444.04 KB)
. From local occlusion cues to global depth estimation. In IEEE Int. Conf. on Acoustics Speech and Signal Processing, ICASSP 2012. Kyoto, Japan; 2012. (480.32 KB)
. Hierarchical Video Representation with Trajectory Binary Partition Tree. In Computer Vision and Pattern Recognition (CVPR). Portland, Oregon; 2013. (4.69 MB)
. Depth order estimation for video frames using motion occlusions. IET Computer Vision. 2014;8(2):152-160. (910.25 KB)
. Monocular Depth Estimation in Images and Sequences using Occlusion Cues. . Signal Theory and Communications. 2014. p. 250. (107.61 MB)
. 2.1 Depth Estimation of Frames in Image Sequences Using Motion Occlusions. In Computer Vision – ECCV 2012. Workshops and Demonstrations. Springer Berlin Heidelberg; 2012. (8.88 MB)
. Monocular Depth Ordering Using Occlusion Cues. Barcelona: Technical University of Catalonia; 2011 .
. End-to-end Convolutional Network for Saliency Prediction. Large-scale Scene Understanding Challenge (LSUN) at CVPR Workshops . Boston, MA (USA): arXiv; 2015 . (1.18 MB)
. SalGAN: Visual Saliency Prediction with Generative Adversarial Networks. In CVPR 2017 Scene Understanding Workshop (SUNw). Honolulu, Hawaii, USA; 2017. (1.85 MB)
. Visual Saliency Prediction using Deep learning Techniques. . 2015. (1.57 MB)
. Shallow and Deep Convolutional Networks for Saliency Prediction. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. Las Vegas, NV, USA: Computer Vision Foundation / IEEE; 2016. (466.13 KB)
. Classification techniques for Alzheimer’s disease early diagnosis. . 2015. (5.86 MB)
. Object-base image coding. Vistas in astronomy. 1997;41:455–461.
. Work in progress - Cooperative and competitive projects for engaging students in advanced ICT subjects. In 41st Annual Frontiers in Education Conference. 2011. pp. 1–3.
. A new approach to active contours for tracking. In IEEE International Conference on Image Processing. 2000.
. Joint region and motion estimation with morphological tools. In International Symposium on Mathematical Morphology, ISMM 1994. Fontainebleau, France; 1994.
. Motion estimation based tracking of active contours. Pattern recognition letters. 2001;22:1447–1456.
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