15,453 research outputs found
Semantic bottleneck for computer vision tasks
This paper introduces a novel method for the representation of images that is
semantic by nature, addressing the question of computation intelligibility in
computer vision tasks. More specifically, our proposition is to introduce what
we call a semantic bottleneck in the processing pipeline, which is a crossing
point in which the representation of the image is entirely expressed with
natural language , while retaining the efficiency of numerical representations.
We show that our approach is able to generate semantic representations that
give state-of-the-art results on semantic content-based image retrieval and
also perform very well on image classification tasks. Intelligibility is
evaluated through user centered experiments for failure detection
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
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping
Many tasks performed by autonomous vehicles such as road marking detection,
object tracking, and path planning are simpler in bird's-eye view. Hence,
Inverse Perspective Mapping (IPM) is often applied to remove the perspective
effect from a vehicle's front-facing camera and to remap its images into a 2D
domain, resulting in a top-down view. Unfortunately, however, this leads to
unnatural blurring and stretching of objects at further distance, due to the
resolution of the camera, limiting applicability. In this paper, we present an
adversarial learning approach for generating a significantly improved IPM from
a single camera image in real time. The generated bird's-eye-view images
contain sharper features (e.g. road markings) and a more homogeneous
illumination, while (dynamic) objects are automatically removed from the scene,
thus revealing the underlying road layout in an improved fashion. We
demonstrate our framework using real-world data from the Oxford RobotCar
Dataset and show that scene understanding tasks directly benefit from our
boosted IPM approach.Comment: equal contribution of first two authors, 8 full pages, 6 figures,
accepted at IV 201
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
Semantic labeling (or pixel-level land-cover classification) in ultra-high
resolution imagery (< 10cm) requires statistical models able to learn high
level concepts from spatial data, with large appearance variations.
Convolutional Neural Networks (CNNs) achieve this goal by learning
discriminatively a hierarchy of representations of increasing abstraction.
In this paper we present a CNN-based system relying on an
downsample-then-upsample architecture. Specifically, it first learns a rough
spatial map of high-level representations by means of convolutions and then
learns to upsample them back to the original resolution by deconvolutions. By
doing so, the CNN learns to densely label every pixel at the original
resolution of the image. This results in many advantages, including i)
state-of-the-art numerical accuracy, ii) improved geometric accuracy of
predictions and iii) high efficiency at inference time.
We test the proposed system on the Vaihingen and Potsdam sub-decimeter
resolution datasets, involving semantic labeling of aerial images of 9cm and
5cm resolution, respectively. These datasets are composed by many large and
fully annotated tiles allowing an unbiased evaluation of models making use of
spatial information. We do so by comparing two standard CNN architectures to
the proposed one: standard patch classification, prediction of local label
patches by employing only convolutions and full patch labeling by employing
deconvolutions. All the systems compare favorably or outperform a
state-of-the-art baseline relying on superpixels and powerful appearance
descriptors. The proposed full patch labeling CNN outperforms these models by a
large margin, also showing a very appealing inference time.Comment: Accepted in IEEE Transactions on Geoscience and Remote Sensing, 201
Regularizing Deep Networks by Modeling and Predicting Label Structure
We construct custom regularization functions for use in supervised training
of deep neural networks. Our technique is applicable when the ground-truth
labels themselves exhibit internal structure; we derive a regularizer by
learning an autoencoder over the set of annotations. Training thereby becomes a
two-phase procedure. The first phase models labels with an autoencoder. The
second phase trains the actual network of interest by attaching an auxiliary
branch that must predict output via a hidden layer of the autoencoder. After
training, we discard this auxiliary branch.
We experiment in the context of semantic segmentation, demonstrating this
regularization strategy leads to consistent accuracy boosts over baselines,
both when training from scratch, or in combination with ImageNet pretraining.
Gains are also consistent over different choices of convolutional network
architecture. As our regularizer is discarded after training, our method has
zero cost at test time; the performance improvements are essentially free. We
are simply able to learn better network weights by building an abstract model
of the label space, and then training the network to understand this
abstraction alongside the original task.Comment: to appear at CVPR 201
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