10,722 research outputs found
Weakly- and Semi-Supervised Panoptic Segmentation
We present a weakly supervised model that jointly performs both semantic- and
instance-segmentation -- a particularly relevant problem given the substantial
cost of obtaining pixel-perfect annotation for these tasks. In contrast to many
popular instance segmentation approaches based on object detectors, our method
does not predict any overlapping instances. Moreover, we are able to segment
both "thing" and "stuff" classes, and thus explain all the pixels in the image.
"Thing" classes are weakly-supervised with bounding boxes, and "stuff" with
image-level tags. We obtain state-of-the-art results on Pascal VOC, for both
full and weak supervision (which achieves about 95% of fully-supervised
performance). Furthermore, we present the first weakly-supervised results on
Cityscapes for both semantic- and instance-segmentation. Finally, we use our
weakly supervised framework to analyse the relationship between annotation
quality and predictive performance, which is of interest to dataset creators.Comment: ECCV 2018. The first two authors contributed equall
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
In this paper, we propose an alternative method to estimate room layouts of
cluttered indoor scenes. This method enjoys the benefits of two novel
techniques. The first one is semantic transfer (ST), which is: (1) a
formulation to integrate the relationship between scene clutter and room layout
into convolutional neural networks; (2) an architecture that can be end-to-end
trained; (3) a practical strategy to initialize weights for very deep networks
under unbalanced training data distribution. ST allows us to extract highly
robust features under various circumstances, and in order to address the
computation redundance hidden in these features we develop a principled and
efficient inference scheme named physics inspired optimization (PIO). PIO's
basic idea is to formulate some phenomena observed in ST features into
mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the
proposed method is more accurate than state-of-the-art methods.Comment: To appear in CVPR 2017. Project Page:
https://sites.google.com/view/st-pio
Visual Chunking: A List Prediction Framework for Region-Based Object Detection
We consider detecting objects in an image by iteratively selecting from a set
of arbitrarily shaped candidate regions. Our generic approach, which we term
visual chunking, reasons about the locations of multiple object instances in an
image while expressively describing object boundaries. We design an
optimization criterion for measuring the performance of a list of such
detections as a natural extension to a common per-instance metric. We present
an efficient algorithm with provable performance for building a high-quality
list of detections from any candidate set of region-based proposals. We also
develop a simple class-specific algorithm to generate a candidate region
instance in near-linear time in the number of low-level superpixels that
outperforms other region generating methods. In order to make predictions on
novel images at testing time without access to ground truth, we develop
learning approaches to emulate these algorithms' behaviors. We demonstrate that
our new approach outperforms sophisticated baselines on benchmark datasets.Comment: to appear at ICRA 201
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