• Corpus ID: 227247712

Chair Segments: A Compact Benchmark for the Study of Object Segmentation

@article{PintoAlva2020ChairSA,
  title={Chair Segments: A Compact Benchmark for the Study of Object Segmentation},
  author={Leticia Pinto-Alva and Ian K. Torres and Rosangel Garcia and Ziyan Yang and Vicente Ordonez},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.01250}
}
Over the years, datasets and benchmarks have had an outsized influence on the design of novel algorithms. In this paper, we introduce ChairSegments, a novel and compact semi-synthetic dataset for object segmentation. We also show empirical findings in transfer learning that mirror recent findings for image classification. We particularly show that models that are fine-tuned from a pretrained set of weights lie in the same basin of the optimization landscape. ChairSegments consists of a diverse… 
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References

SHOWING 1-10 OF 41 REFERENCES
Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene
Rethinking ImageNet Pre-Training
TLDR
Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy, and these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision.
Object Co-segmentation via Graph Optimized-Flexible Manifold Ranking
TLDR
A novel two-stage co-segmentation framework is proposed, which introduces the weak background prior to establish a globally close-loop graph to represent the common object and union background separately and a novel graph optimized-flexible manifold ranking algorithm is proposed to flexibly optimize the graph connection and node labels to co-Segment the common objects.
Mask R-CNN
TLDR
This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
Fully Convolutional Networks for Semantic Segmentation
TLDR
It is shown that convolutional networks by themselves, trained end- to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
Scene Parsing through ADE20K Dataset
TLDR
The ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, is introduced and it is shown that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis.
SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation – A Synthetic Dataset and Baselines
TLDR
This work introduces SAIL-VOS (Semantic Amodal Instance Level Video Object Segmentation), a new dataset aiming to stimulate semantic amodal segmentation research, and presents a synthetic dataset extracted from the photo-realistic game GTA-V.
U-Net: Convolutional Networks for Biomedical Image Segmentation
TLDR
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Crowdsourcing the creation of image segmentation algorithms for connectomics
To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain.
Very Deep Convolutional Networks for Large-Scale Image Recognition
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
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