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9 papers · 1 filter
Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation
Fabio Cermelli, Massimiliano Mancini, Samuel Rota Buló +2
Deep neural networks have enabled major progresses in semantic segmentation. However, even the most advanced neural architectures suffer from important limitations. First, they are…
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives
Yuan Xue, Yuan-Chen Guo, Han Zhang +3
In many applications of computer graphics, art and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph or layout, and have…
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
Jason Y. Zhang, Sam Pepose, Hanbyul Joo +3
We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontro…
What leads to generalization of object proposals?
Rui Wang, Dhruv Mahajan, Vignesh Ramanathan
Object proposal generation is often the first step in many detection models. It is lucrative to train a good proposal model, that generalizes to unseen classes. This could help sca…
Fully Dynamic Inference with Deep Neural Networks
Wenhan Xia, Hongxu Yin, Xiaoliang Dai +1
Modern deep neural networks are powerful and widely applicable models that extract task-relevant information through multi-level abstraction. Their cross-domain success, however, i…
Real-time Semantic Segmentation with Fast Attention
Ping Hu, Federico Perazzi, Fabian Caba Heilbron +4
In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial context (large receptive fields) and fine spatial details (high resolution), both of which…