Search Results for author:
Found 8 papers, 4 papers with code
Interactive Neural Painting
This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.
OneFormer: One Transformer to Rule Universal Image Segmentation
However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance.
Ranked #1 on
Panoptic Segmentation
on COCO minival
AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition
Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e. g., allocating the majority of computation to a task-relevant subset of frames or the most valuable image regions of each frame.
Towards Layer-wise Image Vectorization
Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge.
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy.
SeMask: Semantically Masked Transformers for Semantic Segmentation
To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation.
Ranked #10 on
Semantic Segmentation
on Cityscapes val
Fast Glare Detection in Document Images
Our method divides the document into blocks and collects luminance features from the original image and black-white strokes histograms of the binarized image.
FaSTExt: Fast and Small Text Extractor
The basic component of it is a convolutional neural network which works in a fully-convolutional manner and produces results at multiple scales.
