1,751 research outputs found
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation
learning, yet acquiring dense and pixel-wise ground-truth is both labor- and
time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an
economical alternative, with which training phase could hardly generate
satisfactory performance unfortunately. In order to generate high-quality
annotated data with a low time cost for accurate segmentation, in this paper,
we propose a novel annotation enrichment strategy, which expands existing
coarse annotations of training data to a finer scale. Extensive experiments on
the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural
networks trained with the enriched annotations from our framework yield a
significant improvement over that trained with the original coarse labels. It
is highly competitive to the performance obtained by using human annotated
dense annotations. The proposed method also outperforms among other
state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge
Managemen
Massively Parallel Video Networks
We introduce a class of causal video understanding models that aims to
improve efficiency of video processing by maximising throughput, minimising
latency, and reducing the number of clock cycles. Leveraging operation
pipelining and multi-rate clocks, these models perform a minimal amount of
computation (e.g. as few as four convolutional layers) for each frame per
timestep to produce an output. The models are still very deep, with dozens of
such operations being performed but in a pipelined fashion that enables
depth-parallel computation. We illustrate the proposed principles by applying
them to existing image architectures and analyse their behaviour on two video
tasks: action recognition and human keypoint localisation. The results show
that a significant degree of parallelism, and implicitly speedup, can be
achieved with little loss in performance.Comment: Fixed typos in densenet model definition in appendi
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