126 research outputs found
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
Weakly Supervised Domain-Specific Color Naming Based on Attention
The majority of existing color naming methods focuses on the eleven basic
color terms of the English language. However, in many applications, different
sets of color names are used for the accurate description of objects. Labeling
data to learn these domain-specific color names is an expensive and laborious
task. Therefore, in this article we aim to learn color names from weakly
labeled data. For this purpose, we add an attention branch to the color naming
network. The attention branch is used to modulate the pixel-wise color naming
predictions of the network. In experiments, we illustrate that the attention
branch correctly identifies the relevant regions. Furthermore, we show that our
method obtains state-of-the-art results for pixel-wise and image-wise
classification on the EBAY dataset and is able to learn color names for various
domains.Comment: Accepted at ICPR201
SaliencyGAN: Deep Learning Semisupervised Salient Object Detection in the Fog of IoT
In modern Internet of Things (IoT), visual analysis and predictions are often performed by deep learning models. Salient object detection (SOD) is a fundamental preprocessing for these applications. Executing SOD on the fog devices is a challenging task due to the diversity of data and fog devices. To adopt convolutional neural networks (CNN) on fog-cloud infrastructures for SOD-based applications, we introduce a semisupervised adversarial learning method in this article. The proposed model, named as SaliencyGAN, is empowered by a novel concatenated generative adversarial network (GAN) framework with partially shared parameters. The backbone CNN can be chosen flexibly based on the specific devices and applications. In the meanwhile, our method uses both the labeled and unlabeled data from different problem domains for training. Using multiple popular benchmark datasets, we compared state-of-the-art baseline methods to our SaliencyGAN obtained with 10-100% labeled training data. SaliencyGAN gained performance comparable to the supervised baselines when the percentage of labeled data reached 30%, and outperformed the weakly supervised and unsupervised baselines. Furthermore, our ablation study shows that SaliencyGAN were more robust to the common “mode missing” (or “mode collapse”) issue compared to the selected popular GAN models. The visualized ablation results have proved that SaliencyGAN learned a better estimation of data distributions. To the best of our knowledge, this is the first IoT-oriented semisupervised SOD method
PaintSeg: Training-free Segmentation via Painting
The paper introduces PaintSeg, a new unsupervised method for segmenting
objects without any training. We propose an adversarial masked contrastive
painting (AMCP) process, which creates a contrast between the original image
and a painted image in which a masked area is painted using off-the-shelf
generative models. During the painting process, inpainting and outpainting are
alternated, with the former masking the foreground and filling in the
background, and the latter masking the background while recovering the missing
part of the foreground object. Inpainting and outpainting, also referred to as
I-step and O-step, allow our method to gradually advance the target
segmentation mask toward the ground truth without supervision or training.
PaintSeg can be configured to work with a variety of prompts, e.g. coarse
masks, boxes, scribbles, and points. Our experimental results demonstrate that
PaintSeg outperforms existing approaches in coarse mask-prompt, box-prompt, and
point-prompt segmentation tasks, providing a training-free solution suitable
for unsupervised segmentation
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