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CAP4453 Robot VisionComputer Vision Research PapersObject Recognition from Local Scale-Invariant Features, Lowe, ICCV 1999. [pdf] [code] [other implementations of SIFT] [IJCV]Video Google: A Text Retrieval Approach to Object Matching in Videos, Sivic and Zisserman, ICCV 2003. [pdf] [demo]ImageNet Classification with Deep Convolutional Neural Networks. A. Krizhevsky, I. Sutskever, and G. Hinton. NIPS 2012 [pdf]Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, Lazebnik, Schmid, and Ponce, CVPR 2006. [pdf] [15 scenes dataset] [libpmk] [Matlab]80 Million tiny images: a large dataset for non-parametric object and scene recognition. A. Torralba, R. Fergus, and W. Freeman. PAMI 2008. [pdf]Deep Neural Decision Forests. Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulo. ICCV 2015. [pdf]Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun, CVPR 2016. [pdf] Very deep convolutional networks for large-scale image recognition. K. Simonyan and A. Zisserman, ICLR 2015 [pdf]Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals. F. Xiao and Y. J. Lee. CVPR 2016. [project page] [pdf]Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. R. Girshick, J. Donahue, T. Darrell, J. Malik. CVPR 2013 [pdf] [supp] (see also fast R-CNN, and faster R-CNN)Constrained Parametric Min-Cuts for Automatic Object Segmentation. J. Carreira and C. Sminchisescu. CVPR 2010. [pdf] [code]Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Chen, Papandreou, Kokkinos, Murphy, Yuille. ICLR 2015. [pdf] [code/data]Efficient Hierarchical Graph-Based Video Segmentation. M. Grundmann, V. Kwatra, M. Han, and I. Essa. CVPR 2010. [pdf] [code,demo]Supervoxel-Consistent Foreground Propagation in Video. S. Jain and K. Grauman. ECCV 2014. [pdf] [project page] [data]Selective Search for Object Recognition. J. Uijilings, K. van de Sande, T. Gevers, A. Smeulders. IJCV 2013. [pdf] [project,code]Streaming hierarchical video segmentation. C. Xu, C. Xiong, J. Corso. ECCV 2012. [pdf] [code]A Discriminatively Trained, Multiscale, Deformable Part Model, by P. Felzenszwalb, D. McAllester and D. Ramanan. CVPR 2008. [pdf] [code]Simultaneous Detection and Segmentation. B. Hariharan, P. Arbelaez, R. Girshick, J. Malik. ECCV 2014. [pdf] [code]Learning to Segment Moving Objects in Videos. K. Fragkiadaki, P. Arbelaez, P. Felsen, J. Malik. CVPR 2015 [pdf]Unsupervised Visual Representation Learning by Context Prediction. Carl Doersch, Abhinav Gupta, Alexei Efros. ICCV 2015. [pdf] [web] [code]Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data. J. Wang, Y. Cheng, and R. Feris. CVPR 2016. [pdf]Ambient Sound Provides Supervision for Visual Learning. A. Owens, J. Wu, J. McDermottt, W. Freeman, A. Torralba. ECCV 2016. [pdf]Learning Representations for Automatic Colorization. G. Larsson, M. Maire, and G. Shakhnarovich. ECCV 2016. [pdf] [web] [code] [demo]Visually Indicated Sounds. A. Owens, P. Isola, J. McDermott, A. Torralba, E. Adelson, W. Freeman. CVPR 2016. [pdf] [web/data]Learning Image Representations Equivariant to Ego-motion. D. Jayaraman and K. Grauman. ICCV 2015. [pdf] [web] [slides] [data] [models]Unsupervised learning of visual representations using videos. X. Wang and A. Gupta. ICCV 2015. [pdf] [code] [web]Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video. D. Jayaraman and K. Grauman. CVPR 2016. [pdf]Object-Centric Representation Learning from Unlabeled Videos. R. Gao, D. Jayaraman, and K. Grauman. ACCV 2016. [pdf]Unsupervised learning of visual representations by solving jigsaw puzzles. M. Noroozi and P. Favaro. 2016 [pdf]Learning to See by Moving. P. Agrawal, J. Carreira, J. Malik. ICCV 2015. [pdf]Shuffle and Learn: Unsupervised Learning using Temporal Order Verification. I. Misra, C. Zitnick, M. Hebert. ECCV 2016. [pdf]Visual Recognition with Humans in the Loop. S. Branson, C. Wah, F. Schroff, P. Welinder, P. Perona, S. Belongie ECCV 2010. [pdf] [slides] [code]Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning. Z. Al-Halah, M. Tapaswi, and R. Stiefelhagen. CVPR 2016 [pdf] [features]Relative Attributes. D. Parikh and K. Grauman. ICCV 2011. [pdf] [code/data]City Forensics: Using Visual Elements to Predict Non-Visual City Attributes. Arietta, Efros, Ramammoorthy, Agrawala. Trans on Visualization and Graphics, 2014. [pdf] [web]Street-to-Shop: Cross-Scenario Clothing Retrieval via Parts Alignment and Auxiliary Set. S. Liu, Z. Song, G. Liu, C. Xu, H. Lu, and S. Yan. CVPR 2012. [pdf]Hipster Wars: Discovering Elements of Fashion Styles. M. Kiapour, K. Yamaguchi, A. Berg, and T. Berg. ECCV 2014. [pdf] [game] [dataset]Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer, C. Lampert, H. Nickisch, and S. Harmeling, CVPR 2009 [pdf] [web] [data]End-to-End Localization and Ranking for Relative Attributes. K. Singh and Y. J. Lee. ECCV 2016. [pdf]DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations. Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang. CVPR 2016. [pdf] [web]Discovering the spatial extent of relative attributes. F. Xiao and Y. J. Lee. ICCV 2015. [pdf] [code]Multi-Cue Zero-Shot Learning With Strong Supervision, Zeynep Akata, Mateusz Malinowski, Mario Fritz, Bernt Schiele. CVPR 2016. [pdf]Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression. R. Qiao, L. Liu, C. Shen, and A. van den Hengel. CVPR 2016. [pdf]Fine-Grained Visual Comparisons with Local Learning. A. Yu and K. Grauman. CVPR 2014. [pdf] [supp] [poster] [data] [project page]Photo Aesthetics Ranking Network with Attributes and Content Adaptation. S. Kong, X. Shen, Z. Lin, R. Mech, and C. Fowlkes. ECCV 2016. [pdf]Decorrelating Semantic Visual Attributes by Resisting the Urge to Share. D. Jayaraman, F. Sha, and K. Grauman. CVPR 2014. [pdf] [supp] [project page] [slides] [poster]The Open World of Micro-Videos. P. Nguyen, G. Rogez, C. Fowlkes, and D. Ramanan. 2016 [pdf] Situation Recognition: Visual Semantic Role Labeling for Image Understanding. M. Yatskar, L. Zettlemoyer, and A. Farhadi. [pdf] [demo/data/code]Action Recognition with Improved Trajectories. H. Wang and C. Schmid. ICCV 2013. [pdf] [web/code]SceneGrok: Inferring Action Maps in 3D Environments. M. Savva, A. Chang, P. Hanrahan, M. Fisher, and M. Niebner. [pdf] [web/data]Anticipating Visual Representations from Unlabeled Video. C. Vondrick, H. Pirsiavash, and A. Torralba. CVPR 2016. [pdf]End-to-end learning of action detection from frame glimpses in videos. Yeung, S., Russakovsky, O., Mori, G., Fei-Fei, L. CVPR 2016. [pdf] [web]C3D: Generic features for video analysis. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri. [pdf]Actions~Transformations. X. Wang, A. Farhadi, and A. Gupta. CVPR 2016. [pdf] [data]People Watching: Human Actions as a Cue for Single View Geometry. Fouhey, Delaitre, Gupta, Efros, Laptev, Sivic. ECCV 2012 [pdf] [journal] [web] [slides] [video]Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs. Z. Shou, D. Wang, and S-F. Chang. CVPR 2016. [pdf] [code]Regularizing Long Short Term Memory with 3D Human-Skeleton Sequences for Action Recognition. B. Mahasseni and S. Todorovic. CVPR 2016. [pdf] Activity Forecasting. K. Kitani, B. Ziebart, J. Bagnell and M. Hebert. ECCV 2012. [pdf] [data/code]Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos. F. Heilbron, J. Niebles, and B. Ghanem. CVPR 2016. [pdf] [web] [code]Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search. C-Y. Chen and K. Grauman. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), April 2016. [pdf] [web] [code]Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding. G. Sigurdsson, G. Varol, X. Wang, A. Farhadi, I. Laptev, and A. Gupta. ECCV 2016. [pdf] [web/data]What do 15,000 object categories tell us about classifying and localizing actions? Jain, M., van Gemert, J.C., Snoek, C.G.M. CVPR 2015. [pdf]Two-stream convolutional networks for action recognition in videos. Simonyan, K., Zisserman, A. NIPS 2014. [pdf]Learning to Predict Gaze in Egocentric Video. Y. Li, A. Fathi, and J. Rehg. ICCV 2013. [pdf] [data]KrishnaCam: Using a Longitudinal, Single-Person, Egocentric Dataset for Scene Understanding Tasks. K. Singh, K. Fatahalian, and A. Efros. WACV 2016 [pdf] [web/data]You-Do, I-Learn: Discovering Task Relevant Objects and their Modes of Interaction from Multi-User Egocentric Video. D Damen, T. Leelasawassuk, O Haines, A Calway, W Mayol-Cuevas. BMVC 2014. [pdf] [data]Temporal Segmentation of Egocentric Videos. Y. Poleg, C. Arora, and S. Peleg. CVPR 2014. [pdf] [code/data]Force from Motion: Decoding Physical Sensation from a First Person Video. H.S. Park, J-J. Hwang and J. Shi. CVPR 2016. [pdf] [web/data]An Egocentric Look at Video Photographer Identity, Y. Hoshen and S. Peleg. CVPR 2016. [pdf]Visual Motif Discovery via First-Person Vision. R. Yonetani, K. Kitani, and Y. Sato. ECCV 2016. [pdf]Predicting Important Objects for Egocentric Video Summarization. Y. J. Lee and K. Grauman. IJCV 2015 [pdf] [web]Learning Action Maps of Large Environments via First-Person Vision. N. Rhinehart, K. Kitani. CVPR 2016. [pdf] [slides]Fast Unsupervised Ego-Action Learning for First-person Sports Videos. Kris M. Kitani, Takahiro Okabe, Yoichi Sato, and Akihiro Sugimoto. CVPR 2011 [pdf]Egocentric Future Localization. H. S. Park, J-J. Hwang, Y. Niu, and J. Shi. CVPR 2016. [pdf] [web]Figure-Ground Segmentation Improves Handled Object Recognition in Egocentric Video. X. Ren and C. Gu, CVPR 2010. [pdf] [video] [dataset] Delving into Egocentric Actions, Y. Li, Z. Ye, and J. Rehg. CVPR 2015. [pdf]Detecting Engagement in Egocentric Video. Y-C. Su and K. Grauman. ECCV 2016 [pdf]Understanding Everyday Hands in Action from RGB-D Images. G. Rogez, J. Supancic, D. Ramanan. ICCV 2015. [pdf]Recognizing Activities of Daily Living with a Wrist-mounted Camera. K. Ohnishi, A. Kanehira, A. Kanezaki, and T. Harada. CVPR 2016. [pdf] [poster]PlaceAvoider: Steering First-Person Cameras away from Sensitive Spaces. R. Templeman, M. Korayem, D. Crandall, and A. Kapadia. NDSS 2014. [pdf]Enhancing Lifelogging Privacy by Detecting Screens. M. Korayem, R. Templeman, D. Chen, D. Crandall, and A. Kapadia. CHI 2016. [pdf] [web]EgoSampling: Wide View Hyperlapse from Single and Multiple Egocentric Videos. T. Halperin, Y. Poleg, C. Arora, and S. Peleg. 2016 [pdf]Story-driven Summarization for Egocentric Video. Z. Lu and K. Grauman. CVPR 2013 [pdf]Modeling actions through state changes. A Fathi and J Rehg. CVPR 2013. [pdf]WhittleSearch: Interactive Image Search with Relative Attribute Feedback. A. Kovashka, D. Parikh, and K. Grauman. International Journal on Computer Vision (IJCV), Volume 115, Issue 2, pp 185-210, November 2015. [link] [arxiv] [demo] [project page]Deep Domain Adaptation for Describing People Based on Fine-Grained Clothing Attributes. Q. Chen, J. Huang, R. Feris, L. Brown, J. Dong, S. Yan. CVPR 2015 [pdf]The Curious Robot: Learning Visual Representations via Physical Interactions. L. Pinto, D. Gandhi, Y. Han, Y-L. Park, and A. Gupta. ECCV 2016. [pdf] [web]Pairwise Decomposition of Image Sequences for Active Multi-View Recognition. E. Johns, S. Leutenegger, A. Davison. CVPR 2016. [pdf]Look-Ahead Before You Leap: End-to-End Active Recognition by Forecasting the Effect of Motion. D. Jayaraman and K. Grauman. ECCV 2016. [pdf]Timely object recognition. Karayev, S., Baumgartner, T., Fritz, M., Darrell, T. NIPS 2012 [pdf] [code]3D ShapeNets: A Deep Representation for Volumetric Shape Modeling. Wu et al. CVPR 2015. [pdf] [code/data] [slide]Interactive Perception: Leveraging Action in Perception and Perception in Action. J. Bogh, K. Hausman, B. Sankaran, O. Brock, D. Kragic, S. Schaal, G. Sukhatme. IEEE Trans. on Robotics. 2016. [pdf]Revisiting Active Perception. R. Bajcsy, Y. Aloimonos, J. Tsotsos. 2016. [pdf]Learning to Poke by Poking: Experiential Learning of Intuitive Physics. P. Agrawal, A. Nair, P. Abbeel, J. Malik, S. Levine. 2016 [pdf] [web]Learning attentional policies for tracking and recognition in video with deep networks. L. Bazzani, H. Larochelle, V. Murino, J. Ting, N. de Freitas. ICML 2011. [pdf]Optimal scanning for faster object detection. Butko, N., Movellan, J. CVPR 2009 [pdf]An active search strategy for efficient object detection. Garcia, A.G., Vezhnevets, A., Ferrari, V. CVPR 2015. [pdf]Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video. Y-C. Su and K. Grauman. ECCV 2016. [pdf]Seeing Through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels. I. Misra, C. Zitnick, M. Mitchell, R. Girshick. CVPR 2016 [pdf] [code/models]Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition S. Mathe, C. Sminchisescu. PAMI 2015 [pdf] [data]Understanding and Predicting Image Memorability at a Large Scale. A. Khosla, S. Raju, A. Torralba, and A. Oliva. ICCV 2015. [pdf] [web] [code/data]Learning video saliency from human gaze using candidate selection. D. Rudoy et al. CVPR 2013 [pdf] [web] [video] [code]Learning to Detect a Salient Object. T. Liu et al. CVPR 2007. [pdf] [results] [data] [code]Eye Tracking for Everyone. K. Krafka, A. Khosla, P. Kellnhofer, S. Bhandarkar, W. Matusik and A. Torralba. CVPR 2016. [pdf] [web/data]Training Object Class Detectors from Eye Tracking Data. D. P. Papadopoulos, A. D. F. Clarke, F. Keller and V. Ferrari. ECCV 2014. [pdf] [data]Understanding and Predicting Importance in Images. A. Berg et al. CVPR 2012 [pdf]What's in a Name: First Names as Facial Attributes. H. Chen, A. Gallagher, and B. Girod. CVPR 2013. [pdf] [web/code] [demo] Where are they looking? Khosla, Recasens, Vondrick, Torralba. NIPS 2015. [pdf] [demo] [web]Socially Aware Large-Scale Crowd Forecasting. A. Alahi, V. Ramanathan, L. Fei-Fei. CVPR 2014. [pdf] [data]Social Saliency Prediction. H. S. Park and J. Shi. CVPR 2015 [pdf]Detecting Events and Key Actors in Multi-Person Videos. V. Ramanathan, J. Huang, S. Abu-El-Haija, A. Gorban, K. Murphy, and L. Fei-Fei. CVPR 2016. [pdf]Top-Push Video-based Person Re-identification. J. You, A. Wu, X. Li, W-S. Zheng. CVPR 2016. [pdf]Discovering Groups of People in Images. Choi, Chao, Pantofaru, Savarese. ECCV 2014 [pdf] [web]Detecting People Looking at Each Other in Videos. M. Marin-Jimenez, A. Zisserman, M. Eichner, V. Ferrari. IJCV 2014 [pdf]Sketch2Photo: Internet Image Montage. T. Chen, M-M. Cheng, P. Tan, A. Shamir, S-M. Hu. SIGGRAPH Asia 2009. [pdf] [web]Generation and Comprehension of Unambiguous Object Descriptions. J. Mao, J. Huang, A. Toshev, O. Camburu, A. Yuille, K. Murphy. CVPR 2016. [pdf] [data/web] [code]DeViSE: A Deep Visual-Semantic Embedding Model. A. Frome, G. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, T. Mikolov. NIPS 2013 [pdf]Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images. Malinowski, Rohrbach, Fritz. ICCV 2015. [pdf] [video] [code/data] ................
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