5,909 research outputs found
Deep Learning for Semantic Part Segmentation with High-Level Guidance
In this work we address the task of segmenting an object into its parts, or
semantic part segmentation. We start by adapting a state-of-the-art semantic
segmentation system to this task, and show that a combination of a
fully-convolutional Deep CNN system coupled with Dense CRF labelling provides
excellent results for a broad range of object categories. Still, this approach
remains agnostic to high-level constraints between object parts. We introduce
such prior information by means of the Restricted Boltzmann Machine, adapted to
our task and train our model in an discriminative fashion, as a hidden CRF,
demonstrating that prior information can yield additional improvements. We also
investigate the performance of our approach ``in the wild'', without
information concerning the objects' bounding boxes, using an object detector to
guide a multi-scale segmentation scheme. We evaluate the performance of our
approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing
and face labelling respectively. We show superior performance with respect to
competitive methods that have been extensively engineered on these benchmarks,
as well as realistic qualitative results on part segmentation, even for
occluded or deformable objects. We also provide quantitative and extensive
qualitative results on three classes from the PASCAL Parts dataset. Finally, we
show that our multi-scale segmentation scheme can boost accuracy, recovering
segmentations for finer parts.Comment: 11 pages (including references), 3 figures, 2 table
Long-tailed Instance Segmentation using Gumbel Optimized Loss
Major advancements have been made in the field of object detection and
segmentation recently. However, when it comes to rare categories, the
state-of-the-art methods fail to detect them, resulting in a significant
performance gap between rare and frequent categories. In this paper, we
identify that Sigmoid or Softmax functions used in deep detectors are a major
reason for low performance and are sub-optimal for long-tailed detection and
segmentation. To address this, we develop a Gumbel Optimized Loss (GOL), for
long-tailed detection and segmentation. It aligns with the Gumbel distribution
of rare classes in imbalanced datasets, considering the fact that most classes
in long-tailed detection have low expected probability. The proposed GOL
significantly outperforms the best state-of-the-art method by 1.1% on AP , and
boosts the overall segmentation by 9.0% and detection by 8.0%, particularly
improving detection of rare classes by 20.3%, compared to Mask-RCNN, on LVIS
dataset. Code available at: https://github.com/kostas1515/GOLComment: ECCV202
Automatic coral reef fish identification and 3D measurement in the wild
In this paper we present a pipeline using stereo images in order to
automatically identify, track in 3D fish, and measure fish population.Comment: This paper is in its draft version and should be improved in order to
be published. This paper is issued from one Year of Engineering wor
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning.
Driven by ML applications, the number of different ML inference systems has
exploded. Over 100 organizations are building ML inference chips, and the
systems that incorporate existing models span at least three orders of
magnitude in power consumption and five orders of magnitude in performance;
they range from embedded devices to data-center solutions. Fueling the hardware
are a dozen or more software frameworks and libraries. The myriad combinations
of ML hardware and ML software make assessing ML-system performance in an
architecture-neutral, representative, and reproducible manner challenging.
There is a clear need for industry-wide standard ML benchmarking and evaluation
criteria. MLPerf Inference answers that call. In this paper, we present our
benchmarking method for evaluating ML inference systems. Driven by more than 30
organizations as well as more than 200 ML engineers and practitioners, MLPerf
prescribes a set of rules and best practices to ensure comparability across
systems with wildly differing architectures. The first call for submissions
garnered more than 600 reproducible inference-performance measurements from 14
organizations, representing over 30 systems that showcase a wide range of
capabilities. The submissions attest to the benchmark's flexibility and
adaptability.Comment: ISCA 202
A Benchmark of Long-tailed Instance Segmentation with Noisy Labels (Short Version)
In this paper, we consider the instance segmentation task on a long-tailed
dataset, which contains label noise, i.e., some of the annotations are
incorrect. There are two main reasons making this case realistic. First,
datasets collected from real world usually obey a long-tailed distribution.
Second, for instance segmentation datasets, as there are many instances in one
image and some of them are tiny, it is easier to introduce noise into the
annotations. Specifically, we propose a new dataset, which is a large
vocabulary long-tailed dataset containing label noise for instance
segmentation. Furthermore, we evaluate previous proposed instance segmentation
algorithms on this dataset. The results indicate that the noise in the training
dataset will hamper the model in learning rare categories and decrease the
overall performance, and inspire us to explore more effective approaches to
address this practical challenge. The code and dataset are available in
https://github.com/GuanlinLee/Noisy-LVIS
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