Weakly-Supervised Semantic Segmentation using Motion Cues, Proceedings of the European Conference on Computer Vision (ECCV), p.2016 ,
URL : https://hal.archives-ouvertes.fr/hal-01292794
Learning Motion Patterns in Videos, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.2017 ,
URL : https://hal.archives-ouvertes.fr/hal-01427480
Learning Video Object Segmentation with Visual Memory, Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2017. International Journals-under review ,
URL : https://hal.archives-ouvertes.fr/hal-01511145
Learning to Segment Moving Objects, International Journal on Computer Vision, vol.39, p.105, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01653720
, Learning motion patterns in videos
Slic superpixels compared to state-of-the-art superpixel methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, p.47, 2012. ,
On seeing stuff: the perception of materials by humans and machines, HVEI ,
Learning to see by moving, ICCV, vol.2, pp.0-1 ,
Measuring the objectness of image windows, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1928. ,
Delving deeper into convolutional networks for learning video representations. ICLR, vol.2, pp.0-1 ,
What's the point: Semantic segmentation with point supervision, ECCV, vol.27, p.29, 2016. ,
Shape matching and object recognition using shape contexts, IIEEE Transactions on Pattern Analysis and Machine Intelligence, vol.2, issue.4 ,
Motion segmentation and depth ordering based on morphological segmentation, pp.1-9 ,
Learned-Miller. It's moving! A probabilistic model for causal motion segmentation in moving camera videos, ECCV, vol.74, p.116, 1955. ,
Perception of symmetry in infancy, Developmental Psychology, vol.7, issue.1, pp.1-9 ,
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, ICCV, p.36, 2001. ,
Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.2, issue.3 ,
Video object segmentation by tracking regions, ICCV, p.61, 2009. ,
Object segmentation by long term analysis of point trajectories, ECCV, vol.62, p.118, 2010. ,
Large displacement optical flow: Descriptor matching in variational motion estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.3, issue.3, p.86 ,
One-shot video segmentation, CVPR, vol.52, p.60, 2017. ,
Semantic segmentation with second-order pooling, ECCV, 2012. ,
CPMC: Automatic object segmentation using constrained parametric min-cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.7, p.18, 2012. ,
Semantic image segmentation with deep convolutional nets and fully connected CRFs, ICLR, vol.46, p.110, 2015. ,
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Transactions on Pattern Analysis and Machine Intelligence, p.71, 2017. ,
Global contrast based salient region detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.2, pp.0-1 ,
Learning phrase representations using RNN encoderdecoder for statistical machine translation, EMNLP, vol.65, p.66 ,
URL : https://hal.archives-ouvertes.fr/hal-01433235
Multi-fold MIL training for weakly supervised object localization, CVPR ,
URL : https://hal.archives-ouvertes.fr/hal-00975746
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation, ICCV, 2015. ,
Predictive-corrective networks for action detection, CVPR, vol.2, pp.0-1 ,
Maximum likelihood from incomplete data via the em algorithm, Journal of the royal statistical society. Series B (methodological, vol.8, pp.1-3, 1920. ,
Long-term recurrent convolutional networks for visual recognition and description, CVPR, p.67, 2015. ,
FlowNet: Learning optical flow with convolutional networks, ICCV, vol.69, p.73, 2015. ,
Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary, ECCV, p.25, 2002. ,
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results, vol.38, p.106 ,
Video segmentation by non-local consensus voting, BMVC, vol.94, p.96 ,
Early visual selectivity. Infant perception: From sensation to cognition, vol.1, pp.9-12 ,
Learning hierarchical features for scene labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.3, issue.8, p.31, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00742077
Unsupervised learning for physical interaction through video prediction, NIPS, vol.2, pp.0-1 ,
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, p.55, 1981. ,
Cognitron: A self-organizing multilayered neural network, Biological cybernetics, vol.2, pp.1-2 ,
Semantic video cnns through representation warping, ICCV, vol.2, pp.0-1 ,
Spectral graph reduction for efficient image and streaming video segmentation, CVPR, p.61, 2014. ,
A unified video segmentation benchmark: Annotation, metrics and analysis, ICCV, vol.2, pp.0-1 ,
Understanding the difficulty of training deep feedforward neural networks, AISTATS ,
Generating sequences with recurrent neural networks, vol.2, p.106 ,
Hybrid speech recognition with deep bidirectional LSTM, Workshop on Automatic Speech Recognition and Understanding, vol.2, pp.0-1 ,
DOI : 10.1109/asru.2013.6707742
Speech recognition with deep recurrent neural networks, ICASSP, vol.2, pp.0-1 ,
DOI : 10.1109/icassp.2013.6638947
Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks,1, vol.8, issue.5, p.78, 1961. ,
DOI : 10.1016/j.neunet.2005.06.042
Efficient hierarchical graph based video segmentation, CVPR, p.61, 2010. ,
DOI : 10.1109/cvpr.2010.5539893
URL : http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/pubs/archive/36247.pdf
Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream, Journal of Neuroscience, vol.3, issue.2 ,
Semantic contours from inverse detectors, ICCV, vol.39, p.44, 2011. ,
DOI : 10.1109/iccv.2011.6126343
URL : http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/papers/habmm_iccv2011.pdf
Weakly sup ervised learning of ob ject segmentations from web-scale video, ECCV, 2012. ,
DOI : 10.1007/978-3-642-33863-2_20
URL : http://www.cs.cmu.edu/~rahuls/pub/eccv2012wk-cp-rahuls.pdf
Mask R-CNN, ICCV,2 0 1 7, vol.116, p.118 ,
DOI : 10.1109/tpami.2018.2844175
Identity mappings in deep residual networks, ECCV, vol.1, p.109, 2016. ,
Untersuchungen zu dynamischen neuronalen netzen. Diploma, vol.9, pp.1-9 ,
Long short-term memory, Neural computation, vol.9, issue.8, pp.1-7 ,
Weakly supervised semantic segmentation using web-crawled videos, CVPR, vol.112, p.115, 0111. ,
Neural networks and physical systems with emergent collective computational abilities, Proc. National Academy of Sciences, vol.7, issue.9 ,
Robot vision, p.54, 1986. ,
Computer graphics: principles and practice, p.57, 2014. ,
Flownet 2.0: Evolution of optical flow estimation with deep networks, CVPR, vol.74, p.86, 2017. ,
Batch normalization: Accelerating deep network training by reducing internal covariate shift, ICML, vol.2, pp.0-1 ,
Fusionseg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos, CVPR, vol.58, p.95, 1952. ,
Caffe: Convolutional architecture for fast feature embedding, ACM Multimedia ,
Webly supervised semantic segmentation, CVPR, vol.2, pp.0-1 ,
Transductive inference for text classification using support vector machines, ICML,1, vol.9 ,
Efficient image and video colocalization with Frank-Wolfe algorithm, ECCV, vol.47, p.48 ,
Motion trajectory segmentation via minimum cost multicuts, ICCV, vol.63, p.118, 2011. ,
Simple does it: Weakly supervised instance and semantic segmentation, CVPR, vol.23, p.24, 2005. ,
Lucid data dreaming for object tracking, The 2017 DAVIS Challenge on Video Object Segmentation-CVPR Workshops, vol.2, pp.0-1 ,
Classifier based graph construction for video segmentation, CVPR, vol.61, p.62, 2015. ,
Learning video object segmentation from static images, CVPR, vol.60, p.95, 1952. ,
Principles of Gestalt psychology, Brace Jovanovich, p.62, 1935. ,
Primary object segmentation in videos based on region augmentation and reduction, CVPR, vol.92, p.93, 2017. ,
Seed, expand and constraint: Three principles for weakly-supervised image segmentation, ECCV, vol.104, p.110, 2016. ,
Efficient inference in fully connected CRFs with Gaussian edge potentials, NIPS, vol.23, p.84, 2011. ,
ImageNet classification with deep convolutional neural networks, NIPS, 2012. ,
Ask me anything: Dynamic memory networks for natural language processing, ICML, vol.2, pp.0-1 ,
Unsupervised object discovery and tracking in video collections, ICCV, vol.32, p.48, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01153017
Instance re-identification flow for video object segmentation, The 2017 DAVIS Challenge on Video Object Segmentation-CVPR Workshops, vol.2, pp.0-1 ,
Convolutional networks for images, speech, and time series, vol.3361, p.65, 1995. ,
Backpropagation applied to handwritten zip code recognition, Neural computation, vol.1, issue.4, p.18, 1989. ,
Key-segments for video object segmentation, ICCV, 1964. ,
Deep learning for detecting robotic grasps, The International Journal of Robotics Research, vol.3, issue.4-5, p.18, 2015. ,
Track to the future: Spatio-temporal video segmentation with long-range motion cues, CVPR, p.61, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00817961
Video segmentation by tracking many figure-ground segments, ICCV ,
Deep contrast learning for salient object detection, CVPR, 1928. ,
Video object segmentation with re-identification, The 2017 DAVIS Challenge on Video Object Segmentation-CVPR Workshops, p.119, 2017. ,
Deepsaliency: Multi-task deep neural network model for salient object detection, CVPR, 1928. ,
Fully convolutional instanceaware semantic segmentation, CVPR, vol.116, p.118, 2017. ,
Towards computational baby learning: A weakly-supervised approach for object detection, ICCV ,
Refinenet: Multi-path refinement networks with identity mappings for high-resolution semantic segmentation, CVPR, 2002. ,
Microsoft COCO: Common objects in context, ECCV, p.71 ,
Learning depth from single monocular images using deep convolutional neural fields. IEEE transactions on pattern analysis and machine intelligence, vol.3, p.116, 2016. ,
Fully convolutional networks for semantic segmentation, CVPR, vol.46, p.69, 2015. ,
Sparse modeling for image and vision processing, Foundations and Trends R in Computer Graphics and Vision, vol.8, issue.2-3 ,
URL : https://hal.archives-ouvertes.fr/hal-01081139
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation, CVPR, vol.2, p.90 ,
Recurrent neural network based language model, p.67, 2010. ,
Beyond bounding-boxes: Learning object shape by model-driven grouping, ECCV, p.19, 2012. ,
Feedforward semantic segmentation with zoom-out features, CVPR, vol.2, pp.0-1 ,
Learned-Miller. Coherent motion segmentation in moving camera videos using optical flow orientations, ICCV, vol.85, p.116, 2013. ,
Beyond short snippets: Deep networks for video classification, CVPR, vol.2, pp.0-1 ,
Object segmentation in video: a hierarchical variational approach for turning point tra jectories into dense regions, ICCV, vol.2, pp.0-1 ,
Higher order motion models and spectral clustering, CVPR, 1963. ,
Segmentation of moving objects by long term video analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.3, issue.6, p.118 ,
Exploiting saliency for object segmentation from image level labels, CVPR, vol.104, pp.108-132, 2017. ,
Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, ICCV, vol.46, p.110, 2015. ,
Fast object segmentation in unconstrained video, ICCV, vol.52, p.93, 2013. ,
Constrained convolutional neural networks for weakly supervised segmentation, ICCV, vol.46, p.48, 2015. ,
Fully convolutional multi-class multiple instance learning, ICLR, vol.32, p.46, 2015. ,
Spatio-temporal video autoencoder with differentiable memory, ICLR Workshop track, p.67, 2016. ,
A benchmark dataset and evaluation methodology for video object segmentation, CVPR, vol.79, p.92, 2016. ,
Fully connected object proposals for video segmentation, ICCV, vol.59, p.105, 2015. ,
From image-level to pixel-level labeling with convolutional networks, CVPR, vol.32, p.46, 2015. ,
Learning to refine object segments, ECCV, vol.52, p.75, 2016. ,
Learning object class detectors from weakly annotated video, CVPR, vol.38, p.105, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00695940
Semi-supervised learning of compact document representations with deep networks, ICML, 2002. ,
Faster R-CNN: Towards real-time object detection with region proposal networks, NIPS, p.52, 2015. ,
Tracking as repeated figure/ground segmentation, CVPR ,
EpicFlow: Edge-preserving interpolation of correspondences for optical flow, CVPR, vol.74, p.86, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01142656
Recurrent instance segmentation, ECCV.S p r i n g e r, vol.2, pp.0-1 ,
U-Net: Convolutional networks for biomedical image segmentation, MICCAI, vol.18, p.69, 2001. ,
Grabcut: Interactive foreground extraction using iterated graph cuts, ACM Trans. Graphics, vol.23, issue.3, p.37, 2004. ,
Imagenet large scale visual recognition challenge, International Journal of Computer Vision, vol.1, issue.1 ,
Object-centric spatial pooling for image classification, ECCV, 1925. ,
Convolutional LSTM network: A machine learning approach for precipitation nowcasting, NIPS, vol.2, pp.0-1 ,
Mastering the game of go with deep neural networks and tree search, Nature, vol.5, issue.7, p.9 ,
Two-stream convolutional networks for action recognition in videos, NIPS, vol.71, p.84 ,
Very deep convolutional networks for large-scale image recognition, ICLR, vol.2, pp.0-1 ,
Principles of object perception, Cognitive science, vol.14, issue.1, p.116, 1990. ,
Unsupervised learning of video representations using LSTMs, ICML, vol.2, pp.0-1 ,
End-to-end memory networks, NIPS, vol.2, pp.0-1 ,
Dense point trajectories by GPU-accelerated large displacement optical flow, ECCV, vol.62, p.86, 2010. ,
Causal video object segmentation from persistence of occlusions, CVPR, vol.57, p.80, 2011. ,
COURSERA: Lecture 6.5Neural Networks for Machine Learning, p.83, 2012. ,
Weakly-supervised semantic segmentation using motion cues, ECCV, vol.2, pp.0-1 ,
URL : https://hal.archives-ouvertes.fr/hal-01292794
Learning motion patterns in videos, CVPR, vol.56, p.94, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-01427480
Learning video object segmentation with visual memory, ICCV, vol.2, pp.0-1 ,
URL : https://hal.archives-ouvertes.fr/hal-01511145
Geometric motion segmentation and mo del selection, Phil. Trans. Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol.54, p.55 ,
Video segmentation via object flow, CVPR, vol.59, p.60, 2016. ,
Recurrent fully convolutional networks for video segmentation ,
The devil is in the tails: Fine-grained classification in the wild, p.117, 2017. ,
Weakly supervised structured output learning for semantic segmentation, CVPR, p.16, 2012. ,
Online adaptation of convolutional neural networks for video object segmentation, BMVC, 1960. ,
Interactive video cutout, In ACM Transactions on Graphics (ToG), vol.24, issue.6, pp.585-594, 2005. ,
Saliency-aware geodesic video object segmentation, CVPR,2 0 1 5, vol.63, p.64 ,
Learning to model the tail, Advances in Neural Information Processing Systems, p.117, 2017. ,
Jots: Joint online tracking and segmentation, CVPR, vol.2, pp.0-1 ,
Backpropagation through time: What it do es and how to do it, Proc. IEEE,7, vol.8, p.79 ,
MILCut: A sweeping line multiple instance learning paradigm for interactive image segmentation, CVPR ,
Google's neural machine translation system: Bridging the gap between human and machine translation, vol.2, pp.0-1 ,
Semantic segmentation without annotating segments, ICCV, vol.5, p.23, 2013. ,
LIBSVX: A supervoxel library and benchmark for early video processing, International Journal of Computer Vision, p.61, 2016. ,
Tell me what you see and I will show you where it is, CVPR, p.25, 2014. ,
Using goal-driven deep learning models to understand sensory cortex, Nature neuroscience, vol.19, issue.3, pp.356-365, 2016. ,
Video object segmentation through spatially accurate and temporally dense extraction of primary object regions, CVPR, vol.2, pp.0-1 ,
Semantic object segmentation via detection in weakly labeled video, CVPR, vol.2, pp.0-1 ,
Open vocabulary scene parsing, ICCV, vol.2, pp.0-1 ,
Pyramid scene parsing network, CVPR, 2002. ,
, Conditional random fields as recurrent neural networks. In ICCV, vol.31, p.46, 2015.
Interpreting deep visual representations via network dissection, CVPR, vol.2, pp.0-1 ,
Learning deep features for discriminative localization, CVPR, vol.48, p.115, 2016. ,
Learning from weakly supervised data by the expectation loss svm (e-svm) algorithm, NIPS, vol.19, p.23 ,
Capturing long-tail distributions of object subcategories, CVPR ,