780 research outputs found
An XPS study of the thermal degradation of polystyrene-clay nanocomposites
X-ray photoelectron spectroscopy, XPS, has been used to examine several polystyrene-clay nanocomposites. The accumulation of oxygen, from the almuniosilicate, on the surface of the polymer was observed, along with the loss of carbon. This confirms that the barrier properties of the clay provide a mechanism by which nanocomposite formation can enhance the fire retardancy of the polymers. No difference is detected depending upon the extent of exfoliation or intercalation of the nanocomposite. #2002 Elsevier Science Ltd. All rights reserved
An XPS Investigation of Thermal Degradation and Charring of PMMA Clay Nanocomposites
Poly(methyl methacrylate)–clay nanocomposites have been studied using X-ray photoelectron spectroscopy. It is clear that as the polymer undergoes thermal degradation, the clay accumulates at the surface and the barrier properties which result from this clay accumulation have been described as the reason for the decreased heat release rate for nanocomposites. The surface composition of the clay changes as the nanocomposite is heated and the changes are affected by the organic-modification that were applied to the clay in order to prepare the nanocomposite
Multi-Label Self-Supervised Learning with Scene Images
Self-supervised learning (SSL) methods targeting scene images have seen a
rapid growth recently, and they mostly rely on either a dedicated dense
matching mechanism or a costly unsupervised object discovery module. This paper
shows that instead of hinging on these strenuous operations, quality image
representations can be learned by treating scene/multi-label image SSL simply
as a multi-label classification problem, which greatly simplifies the learning
framework. Specifically, multiple binary pseudo-labels are assigned for each
input image by comparing its embeddings with those in two dictionaries, and the
network is optimized using the binary cross entropy loss. The proposed method
is named Multi-Label Self-supervised learning (MLS). Visualizations
qualitatively show that clearly the pseudo-labels by MLS can automatically find
semantically similar pseudo-positive pairs across different images to
facilitate contrastive learning. MLS learns high quality representations on
MS-COCO and achieves state-of-the-art results on classification, detection and
segmentation benchmarks. At the same time, MLS is much simpler than existing
methods, making it easier to deploy and for further exploration.Comment: ICCV202
Instance-based Max-margin for Practical Few-shot Recognition
In order to mimic the human few-shot learning (FSL) ability better and to
make FSL closer to real-world applications, this paper proposes a practical FSL
(pFSL) setting. pFSL is based on unsupervised pretrained models (analogous to
human prior knowledge) and recognizes many novel classes simultaneously.
Compared to traditional FSL, pFSL is simpler in its formulation, easier to
evaluate, more challenging and more practical. To cope with the rarity of
training examples, this paper proposes IbM2, an instance-based max-margin
method not only for the new pFSL setting, but also works well in traditional
FSL scenarios. Based on the Gaussian Annulus Theorem, IbM2 converts random
noise applied to the instances into a mechanism to achieve maximum margin in
the many-way pFSL (or traditional FSL) recognition task. Experiments with
various self-supervised pretraining methods and diverse many- or few-way FSL
tasks show that IbM2 almost always leads to improvements compared to its
respective baseline methods, and in most cases the improvements are
significant. With both the new pFSL setting and novel IbM2 method, this paper
shows that practical few-shot learning is both viable and promising
Quantized Feature Distillation for Network Quantization
Neural network quantization aims to accelerate and trim full-precision neural
network models by using low bit approximations. Methods adopting the
quantization aware training (QAT) paradigm have recently seen a rapid growth,
but are often conceptually complicated. This paper proposes a novel and highly
effective QAT method, quantized feature distillation (QFD). QFD first trains a
quantized (or binarized) representation as the teacher, then quantize the
network using knowledge distillation (KD). Quantitative results show that QFD
is more flexible and effective (i.e., quantization friendly) than previous
quantization methods. QFD surpasses existing methods by a noticeable margin on
not only image classification but also object detection, albeit being much
simpler. Furthermore, QFD quantizes ViT and Swin-Transformer on MS-COCO
detection and segmentation, which verifies its potential in real world
deployment. To the best of our knowledge, this is the first time that vision
transformers have been quantized in object detection and image segmentation
tasks.Comment: AAAI202
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