Publication
Found 53 results
Author Title Type [ Year
] Filters: First Letter Of Last Name is X [Clear All Filters]
The Compositional Nature of Event Representations in the Human Brain. (2014).
CBMM Memo 011.pdf (3.95 MB)
Predicting Saliency Beyond Pixels. (2014). at <http://www.ece.nus.edu.sg/stfpage/eleqiz/predicting.html>
Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions. (2014).
CBMM Memo 012.pdf (678.95 KB)
Computer Vision – ECCV 2014, Lecture Notes in Computer Science 8693, 612–627 (Springer International Publishing, 2014).
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN). (2015).
CBMM Memo 033.pdf (839.42 KB)
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images. International Conference of Computer Vision (2015). at <www.stat.ucla.edu/~junhua.mao/projects/child_learning.html>
child_learning_iccv2015.pdf (1.16 MB)
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images. NIPS 2016 (2016).
6590-training-and-evaluating-multimodal-word-embeddings-with-large-scale-web-annotated-images.pdf (1.57 MB)
Detecting Semantic Parts on Partially Occluded Objects. (2017).
CBMM-Memo-078.pdf (1.74 MB)
Detecting Semantic Parts on Partially Occluded Objects. (2017).
CBMM-Memo-078.pdf (1.74 MB)
Detecting Semantic Parts on Partially Occluded Objects. British Machine Vision Conference (BMVC) (2017). at <https://bmvc2017.london/proceedings/>
Detecting Semantic Parts on Partially Occluded Objects. British Machine Vision Conference (BMVC) (2017). at <https://bmvc2017.london/proceedings/>
Human Learning in Atari. AAAI Spring Symposium Series (2017).
Tsividis et al - Human Learning in Atari.pdf (844.47 KB)
MarrNet: 3D Shape Reconstruction via 2.5D Sketches. Advances in Neural Information Processing Systems 30 540–550 (2017). at <http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches.pdf>
MarrNet: 3D Shape Reconstruction via 2.5D Sketches (6.25 MB)
Biologically-plausible learning algorithms can scale to large datasets. (2018).
CBMM-Memo-092.pdf (1.31 MB)
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion. (2018).
CBMM-Memo-083.pdf (2.32 MB)
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion. (2018).
CBMM-Memo-083.pdf (2.32 MB)
DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>
DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>
Single-Shot Object Detection with Enriched Semantics. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>
Single-Shot Object Detection with Enriched Semantics. (2018).
CBMM-Memo-084.pdf (1.92 MB)
Visual concepts and compositional voting. (2018).
CBMM-Memo-087.pdf (3.37 MB)
Visual concepts and compositional voting. (2018).
CBMM-Memo-087.pdf (3.37 MB)
Visual Concepts and Compositional Voting. Annals of Mathematical Sciences and Applications (AMSA) 3, 151–188 (2018).