34,182 research outputs found
Adaptive Rotated Convolution for Rotated Object Detection
Rotated object detection aims to identify and locate objects in images with
arbitrary orientation. In this scenario, the oriented directions of objects
vary considerably across different images, while multiple orientations of
objects exist within an image. This intrinsic characteristic makes it
challenging for standard backbone networks to extract high-quality features of
these arbitrarily orientated objects. In this paper, we present Adaptive
Rotated Convolution (ARC) module to handle the aforementioned challenges. In
our ARC module, the convolution kernels rotate adaptively to extract object
features with varying orientations in different images, and an efficient
conditional computation mechanism is introduced to accommodate the large
orientation variations of objects within an image. The two designs work
seamlessly in rotated object detection problem. Moreover, ARC can conveniently
serve as a plug-and-play module in various vision backbones to boost their
representation ability to detect oriented objects accurately. Experiments on
commonly used benchmarks (DOTA and HRSC2016) demonstrate that equipped with our
proposed ARC module in the backbone network, the performance of multiple
popular oriented object detectors is significantly improved (e.g. +3.03% mAP on
Rotated RetinaNet and +4.16% on CFA). Combined with the highly competitive
method Oriented R-CNN, the proposed approach achieves state-of-the-art
performance on the DOTA dataset with 81.77% mAP
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model
geometric transformations due to the fixed geometric structures in its building
modules. In this work, we introduce two new modules to enhance the
transformation modeling capacity of CNNs, namely, deformable convolution and
deformable RoI pooling. Both are based on the idea of augmenting the spatial
sampling locations in the modules with additional offsets and learning the
offsets from target tasks, without additional supervision. The new modules can
readily replace their plain counterparts in existing CNNs and can be easily
trained end-to-end by standard back-propagation, giving rise to deformable
convolutional networks. Extensive experiments validate the effectiveness of our
approach on sophisticated vision tasks of object detection and semantic
segmentation. The code would be released
Deformable Object Tracking with Gated Fusion
The tracking-by-detection framework receives growing attentions through the
integration with the Convolutional Neural Networks (CNNs). Existing
tracking-by-detection based methods, however, fail to track objects with severe
appearance variations. This is because the traditional convolutional operation
is performed on fixed grids, and thus may not be able to find the correct
response while the object is changing pose or under varying environmental
conditions. In this paper, we propose a deformable convolution layer to enrich
the target appearance representations in the tracking-by-detection framework.
We aim to capture the target appearance variations via deformable convolution,
which adaptively enhances its original features. In addition, we also propose a
gated fusion scheme to control how the variations captured by the deformable
convolution affect the original appearance. The enriched feature representation
through deformable convolution facilitates the discrimination of the CNN
classifier on the target object and background. Extensive experiments on the
standard benchmarks show that the proposed tracker performs favorably against
state-of-the-art methods
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
Beyond the existing single-person and multiple-person human parsing tasks in
static images, this paper makes the first attempt to investigate a more
realistic video instance-level human parsing that simultaneously segments out
each person instance and parses each instance into more fine-grained parts
(e.g., head, leg, dress). We introduce a novel Adaptive Temporal Encoding
Network (ATEN) that alternatively performs temporal encoding among key frames
and flow-guided feature propagation from other consecutive frames between two
key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the
instance-level parsing result for each key frame, which integrates both the
global human parsing and instance-level human segmentation into a unified
model. To balance between accuracy and efficiency, the flow-guided feature
propagation is used to directly parse consecutive frames according to their
identified temporal consistency with key frames. On the other hand, ATEN
leverages the convolution gated recurrent units (convGRU) to exploit temporal
changes over a series of key frames, which are further used to facilitate the
frame-level instance-level parsing. By alternatively performing direct feature
propagation between consistent frames and temporal encoding network among key
frames, our ATEN achieves a good balance between frame-level accuracy and time
efficiency, which is a common crucial problem in video object segmentation
research. To demonstrate the superiority of our ATEN, extensive experiments are
conducted on the most popular video segmentation benchmark (DAVIS) and a newly
collected Video Instance-level Parsing (VIP) dataset, which is the first video
instance-level human parsing dataset comprised of 404 sequences and over 20k
frames with instance-level and pixel-wise annotations.Comment: To appear in ACM MM 2018. Code link:
https://github.com/HCPLab-SYSU/ATEN. Dataset link: http://sysu-hcp.net/li
User Constrained Thumbnail Generation using Adaptive Convolutions
Thumbnails are widely used all over the world as a preview for digital
images. In this work we propose a deep neural framework to generate thumbnails
of any size and aspect ratio, even for unseen values during training, with high
accuracy and precision. We use Global Context Aggregation (GCA) and a modified
Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails
in real time. GCA is used to selectively attend and aggregate the global
context information from the entire image while the RPN is used to predict
candidate bounding boxes for the thumbnail image. Adaptive convolution
eliminates the problem of generating thumbnails of various aspect ratios by
using filter weights dynamically generated from the aspect ratio information.
The experimental results indicate the superior performance of the proposed
model over existing state-of-the-art techniques.Comment: International Conference on Acoustics, Speech, and Signal
Processing(ICASSP), 201
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