250 research outputs found
A Simple Parametric Classification Baseline for Generalized Category Discovery
Generalized category discovery (GCD) is a problem setting where the goal is
to discover novel categories within an unlabelled dataset using the knowledge
learned from a set of labelled samples. Recent works in GCD argue that a
non-parametric classifier formed using semi-supervised -means can outperform
strong baselines which use parametric classifiers as it can alleviate the
over-fitting to seen categories in the labelled set. In this paper, we revisit
the reason that makes previous parametric classifiers fail to recognise new
classes for GCD. By investigating the design choices of parametric classifiers
from the perspective of model architecture, representation learning, and
classifier learning, we conclude that the less discriminative representations
and unreliable pseudo-labelling strategy are key factors that make parametric
classifiers lag behind non-parametric ones. Motivated by our investigation, we
present a simple yet effective parametric classification baseline that
outperforms the previous best methods by a large margin on multiple popular GCD
benchmarks. We hope the investigations and the simple baseline can serve as a
cornerstone to facilitate future studies. Our code is available at:
https://github.com/CVMI-Lab/SimGCD.Comment: Code: https://github.com/CVMI-Lab/SimGC
Implementing the CSILE/KB Program of University of Toronto in English Teaching in China
This paper first proposes that Aims of English Teaching should go beyond communicative competence according to Bloom's taxonomy. Then it mainly analyzes that teaching English as a foreign language in China can learn from CSILE/KB Program of University of Toronto in terms of goal setting, active roles of thinking scaffolding and comprehensive English competence acquirement.To bring TEFL to a new stage,the integration of TEFL with KB and MOOCS is put forward and some suggestions are made in the end
Global Texture Enhancement for Fake Face Detection in the Wild
Generative Adversarial Networks (GANs) can generate realistic fake face
images that can easily fool human beings.On the contrary, a common
Convolutional Neural Network(CNN) discriminator can achieve more than 99.9%
accuracyin discerning fake/real images. In this paper, we conduct an empirical
study on fake/real faces, and have two important observations: firstly, the
texture of fake faces is substantially different from real ones; secondly,
global texture statistics are more robust to image editing and transferable to
fake faces from different GANs and datasets. Motivated by the above
observations, we propose a new architecture coined as Gram-Net, which leverages
global image texture representations for robust fake image detection.
Experimental results on several datasets demonstrate that our Gram-Net
outperforms existing approaches. Especially, our Gram-Netis more robust to
image editings, e.g. down-sampling, JPEG compression, blur, and noise. More
importantly, our Gram-Net generalizes significantly better in detecting fake
faces from GAN models not seen in the training phase and can perform decently
in detecting fake natural images
DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic Segmentation
Deep learning approaches achieve prominent success in 3D semantic
segmentation. However, collecting densely annotated real-world 3D datasets is
extremely time-consuming and expensive. Training models on synthetic data and
generalizing on real-world scenarios becomes an appealing alternative, but
unfortunately suffers from notorious domain shifts. In this work, we propose a
Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and
context gaps caused by different sensing mechanisms and layout placements
across domains. Our DODA encompasses virtual scan simulation to imitate
real-world point cloud patterns and tail-aware cuboid mixing to alleviate the
interior context gap with a cuboid-based intermediate domain. The first
unsupervised sim-to-real adaptation benchmark on 3D indoor semantic
segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 7 popular
Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA
approaches by over 13% on both 3D-FRONT ScanNet and 3D-FRONT
S3DIS. Code will be available
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