2,217 research outputs found
Res2Net: A New Multi-scale Backbone Architecture
Representing features at multiple scales is of great importance for numerous
vision tasks. Recent advances in backbone convolutional neural networks (CNNs)
continually demonstrate stronger multi-scale representation ability, leading to
consistent performance gains on a wide range of applications. However, most
existing methods represent the multi-scale features in a layer-wise manner. In
this paper, we propose a novel building block for CNNs, namely Res2Net, by
constructing hierarchical residual-like connections within one single residual
block. The Res2Net represents multi-scale features at a granular level and
increases the range of receptive fields for each network layer. The proposed
Res2Net block can be plugged into the state-of-the-art backbone CNN models,
e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these
models and demonstrate consistent performance gains over baseline models on
widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies
and experimental results on representative computer vision tasks, i.e., object
detection, class activation mapping, and salient object detection, further
verify the superiority of the Res2Net over the state-of-the-art baseline
methods. The source code and trained models are available on
https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.Comment: Technical Repor
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