12,125 research outputs found
AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network
Recently, methods based on deep learning have been successfully applied to
ship detection for synthetic aperture radar (SAR) images. Despite the
development of numerous ship detection methodologies, detecting small and
coastal ships remains a significant challenge due to the limited features and
clutter in coastal environments. For that, a novel adaptive multi-hierarchical
attention module (AMAM) is proposed to learn multi-scale features and
adaptively aggregate salient features from various feature layers, even in
complex environments. Specifically, we first fuse information from adjacent
feature layers to enhance the detection of smaller targets, thereby achieving
multi-scale feature enhancement. Then, to filter out the adverse effects of
complex backgrounds, we dissect the previously fused multi-level features on
the channel, individually excavate the salient regions, and adaptively
amalgamate features originating from different channels. Thirdly, we present a
novel adaptive multi-hierarchical attention network (AMANet) by embedding the
AMAM between the backbone network and the feature pyramid network (FPN).
Besides, the AMAM can be readily inserted between different frameworks to
improve object detection. Lastly, extensive experiments on two large-scale SAR
ship detection datasets demonstrate that our AMANet method is superior to
state-of-the-art methods.Comment: 11 pages, 7 figure
PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
Contexts play an important role in the saliency detection task. However,
given a context region, not all contextual information is helpful for the final
task. In this paper, we propose a novel pixel-wise contextual attention
network, i.e., the PiCANet, to learn to selectively attend to informative
context locations for each pixel. Specifically, for each pixel, it can generate
an attention map in which each attention weight corresponds to the contextual
relevance at each context location. An attended contextual feature can then be
constructed by selectively aggregating the contextual information. We formulate
the proposed PiCANet in both global and local forms to attend to global and
local contexts, respectively. Both models are fully differentiable and can be
embedded into CNNs for joint training. We also incorporate the proposed models
with the U-Net architecture to detect salient objects. Extensive experiments
show that the proposed PiCANets can consistently improve saliency detection
performance. The global and local PiCANets facilitate learning global contrast
and homogeneousness, respectively. As a result, our saliency model can detect
salient objects more accurately and uniformly, thus performing favorably
against the state-of-the-art methods
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch
In this work we introduce a cross modal image retrieval system that allows
both text and sketch as input modalities for the query. A cross-modal deep
network architecture is formulated to jointly model the sketch and text input
modalities as well as the the image output modality, learning a common
embedding between text and images and between sketches and images. In addition,
an attention model is used to selectively focus the attention on the different
objects of the image, allowing for retrieval with multiple objects in the
query. Experiments show that the proposed method performs the best in both
single and multiple object image retrieval in standard datasets.Comment: Accepted at ICPR 201
Hierarchical Attention Network for Action Segmentation
The temporal segmentation of events is an essential task and a precursor for
the automatic recognition of human actions in the video. Several attempts have
been made to capture frame-level salient aspects through attention but they
lack the capacity to effectively map the temporal relationships in between the
frames as they only capture a limited span of temporal dependencies. To this
end we propose a complete end-to-end supervised learning approach that can
better learn relationships between actions over time, thus improving the
overall segmentation performance. The proposed hierarchical recurrent attention
framework analyses the input video at multiple temporal scales, to form
embeddings at frame level and segment level, and perform fine-grained action
segmentation. This generates a simple, lightweight, yet extremely effective
architecture for segmenting continuous video streams and has multiple
application domains. We evaluate our system on multiple challenging public
benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech
Egocentric datasets, and achieves state-of-the-art performance. The evaluated
datasets encompass numerous video capture settings which are inclusive of
static overhead camera views and dynamic, ego-centric head-mounted camera
views, demonstrating the direct applicability of the proposed framework in a
variety of settings.Comment: Published in Pattern Recognition Letter
Real Time Image Saliency for Black Box Classifiers
In this work we develop a fast saliency detection method that can be applied
to any differentiable image classifier. We train a masking model to manipulate
the scores of the classifier by masking salient parts of the input image. Our
model generalises well to unseen images and requires a single forward pass to
perform saliency detection, therefore suitable for use in real-time systems. We
test our approach on CIFAR-10 and ImageNet datasets and show that the produced
saliency maps are easily interpretable, sharp, and free of artifacts. We
suggest a new metric for saliency and test our method on the ImageNet object
localisation task. We achieve results outperforming other weakly supervised
methods
- …