7 citations · 12 across the 8 of their papers we have counts for
8 papers · 1 filter
Towards Open-World Segmentation of Parts
Tai-Yu Pan, Qing Liu, Wei-Lun Chao +1
Segmenting object parts such as cup handles and animal bodies is important in many real-world applications but requires more annotation effort. The largest dataset nowadays contain…
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
Qing Liu, Vignesh Ramanathan, Dhruv Mahajan +2
Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predomina…
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion
Adam Kortylewski, Qing Liu, Angtian Wang +2
Computer vision systems in real-world applications need to be robust to partial occlusion while also being explainable. In this work, we show that black-box deep convolutional neur…
Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion
Adam Kortylewski, Ju He, Qing Liu +1
Recent findings show that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying…
Localizing Occluders with Compositional Convolutional Networks
Adam Kortylewski, Qing Liu, Huiyu Wang +2
Compositional convolutional networks are generative compositional models of neural network features, that achieve state of the art results when classifying partially occluded objec…
Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion
Adam Kortylewski, Qing Liu, Huiyu Wang +2
Deep convolutional neural networks (DCNNs) are powerful models that yield impressive results at object classification. However, recent work has shown that they do not generalize we…