Presentations will be evaluated based on clarity. In particular, you should explain related earlier work discussed in the paper, and then, using this prior work background/context, explain the main contribution/idea of the paper. Note that many of the papers in the list are selected in part becasue they have a good overview of related work. You can also search for follow up works and present those.

• Weakly supervised instance segmentation using the bounding box tightness prior
  C.C.Hsu, K.J.Hsu, C.C.Tsai, Y.Y.Lin; In NeurIPS, 2019
• Weakly supervised instance segmentation by learning annotation consistent instances
  A.Arun, C.V.Jawahar, M.P.Kumar; In European Conference on Computer Vision (ECCV) 2020
• SOLO: Segmenting Objects by Locations
  X.Wang, T.Kong, C.Shen, Y.Jiang, L.Li; In European Conference on Computer Vision (ECCV) 2020
• Semantic Instance Segmentation with a Discriminative Loss Function
  B. De Brabandere, D.Neven, L. Van Gool; In ArXiv 2017
• PolyTransform: Deep Polygon Transformer for Instance Segmentation
  J.Liang, N.Homayounfar, W.-C.Ma, Y.Xiong, R.Hu, R.Urtasun; In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
• PolarMask: Single Shot Instance Segmentation With Polar Representation
  E.Xie, P.Sun, X.Song, W.Wang, X.Liu, D.Liang, C.Shen, P.Luo; In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
• Panoptic Feature Pyramid Networks
  A.Kirillov, R.Girshick, K.He, P.Dollar; In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
• Learning to Segment Every Thing
  R.Hu, P.Dollar, K.He, T.Darrell, R.Girshick; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
• Deep Watershed Transform for Instance Segmentation
  M.Bai, R.Urtasun; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
• Fully Convolutional Instance-Aware Semantic Segmentation
  Y.Li, H.Qi, J.Dai, X.Ji, Y.Wei; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
• Weakly-Supervised Salient Object Detection via Scribble Annotations
  J.Zhang, X.Yu, A.Li, P.Song, B.Liu, Y.Dai; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
• F-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation
  K.Sofiiuk, I.Petrov, O.Barinova, A.Konushin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
• Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization
  R.R.Selvaraju, M.Cogswell, A.Das, R.V.D.Parikh, D.Batra; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017 (also IJCV 2019)
• Conditional Networks for Few-Shot Semantic Segmentation
  K.Rakelly, E.Shelhamer, T.Darrell, A.Efros, S.Levine
• Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
  D.Pathak, P.Krahenbuhl, T.Darrell; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015
• Consistent Structural Relation Learning for Zero-Shot Segmentation
  P.Li, Y.Wei, Y. Yang; In the Proceedings of NeurIPS, 2020
• Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
  H.Kervadec, J.Dolz, S.Wang, E.Granger, I.Ben Ayed; In the Proceedings of the Third Conference on Medical Imaging with Deep Learning, MIDL, 2020
• Self-labelling via simultaneous clustering and representation learning
  Y.M.Asano, C.Rupprecht, A.Vedaldi; At the International Conference on Learning Representations (ICLR) 2020
• Self-Supervised Classification Network
  E.Amrani, L.Karlinsky, A.Bronstein, In ArXiv 2021
• Invariant Information Clustering for Unsupervised Image Classification and Segmentation
  X.Ji, J.F. Henriques, A.Vedaldi; Proceedings of the International Conference on Computer Vision (ICCV), 2019
• ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
  D.Lin, J.Dai, J.Jia, K.He, J.Sun; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
• BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
  J.Dai, K.He, J.Sun; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015
• Zero-Shot Semantic Segmentation
  M.Bucher, T.-H.Vu, M.Cord, P.Perez; In the Proceedings of NeurIPS, 2019
• Zero-Shot Object Detection
  A.Bansal, K.Sikka, G.Sharma, R.Chellappa, A.Divakaran; Proceedings of the European Conference on Computer Vision (ECCV), 2018
• Single-Stage Semantic Segmentation From Image Labels
  Nikita Araslanov, Stefan Roth; Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2020
• Conditional Random Fields as Recurrent Neural Networks
  S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, Dalong Du, C. Huang, P.H.S. Torr; International Conference on Computer Vision (ICCV), 2015.
• CRF-CNN: Modeling Structured Information in Human Pose Estimation
  X. Chu, W. Ouyang, H. Li, X. Wang; Neural Information Processing Systems (NIPS), 2016
• Coloful image colorization
  R. Zhang, P. Isola, A. Efros; European Conference on Computer Vision (ECCV), 2016.
• Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
  A. Kolesnikov, C.H. Lampert; European Conference on Computer Vision (ECCV), 2016
• Unsupervised Visual Representation Learning by Context Prediction
  C. Doersch, A. Gupta, and A. A. Efros; International Conference on Computer Vision (ICCV), 2015
• Multi-View Consistency as Supervisory Signal for Learning Shape and Pose Prediction
  S.Tulsiani, A. Efros, J. Malik; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
• Learning Single-View 3D Reconstruction with Limited Pose Supervision
  G.Yang, Y.Cui, S.Belongie, B. Hariharan; The European Conference on Computer Vision (ECCV), 2018.
• BB8: a scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth
  M. Rad, V. Lepetit; International Conference on Computer Vision (ICCV), 2017
• 3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
  A. Grabner, P. Roth, V. Lepetit. In IEEE conferenc on Computer Vision and Pattern Recognition (CVPR), 2018
• Generalized Feedback Loop for Joint Hand-Object Pose Estimation
  M. Oberweger, P. Wohlhart, V Lepetit. IEEE Transactions for Pattern Analysis and Machine Intelligence (PAMI), 2019.
• Unsupervised monocular depth estimation with left-right consistency
  C. Godard, O. Mac Aodha, G. Brostow; IEEE conference on Computer Vision and Pattern Recognition (CVPR) 2017
• ProxQuant: Quantized Neural Networks via Proximal Operators
  Y. Bai, Y.-X. Wang, E. Liberty; ICLR 2019
• Dissimilarity Coefficient based Weakly Supervised Object Detection
  A. Arun, C. V. Jawahar, P. Kumar; In IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2019
• Deep Image Prior
  V. Lempitsky, A. Vedaldi, D. Ulyanov; In IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2018
• Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors
  Y. Gandelsman, A. Shocher, M. Irani; In IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2019
• Relation Networks for Object Detection
  H. Hu, J. Gu, Z. Zhang, J. Dai, Y. Wei; In IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2018
• Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
  E. Zakharov, A.Shysheya1, E. Burkov, V. Lempitsky; ArXiv, May 2019.
• The Lovasz-Softmax Loss: A tractable surrogate for the optimisation of the intersection-over-union metric in neural networks
  M. Berman, A. R. Triki, M. Blaschko; In IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2018
• Higher Order Conditional Random Fields in Deep Neural Networks
  A. Arnab, S. Jayasumana, S. Zheng, P.H.S. Torr; In European Conference on Computer Vision (ECCV), 2016.
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