Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat

Insufficient

DFFCN: Dual Flow Fusion Convolutional Network for?Micro Expression Recognitionotherapy. However, the short duration and subtle movement of facial muscles make it difficult to extract micro-expression features. In this article, we propose a Dual Flow Fusion Convolutional Network (DFFCN) that combines the learning flow and optical flow to capture spatiotemporal features. Specif

爆米花

AUPro: Multi-label Facial Action Unit Proposal Generation for?Sequence-Level Analysisclassification results provided by previous work are not explicit enough for the analysis required by many real-world applications, and as AU is a dynamic process, sequence-level analysis maintaining a global view has yet been gravely ignored in the literature. To fill in the blank, we propose a mul

甜食

Deep Kernelized Network for?Fine-Grained Recognitionen the original data is not linearly separable. In this paper, we focus on this issue by investigating the impact of using higher order kernels. For this purpose, we replace convolution layers with Kervolution layers proposed in?[.]. Similarly, we replace fully connected layers alternatively with Ke

crease

beta-cells

Open-Set Recognition with?Dual Probability Learningt unknown samples while maintaining high classification accuracy on the known classes. Previous methods are divided into two stages, including open-set identification and closed-set classification. These methods usually reject unknown samples according to the previous analysis of the known classes.

后退

How Much Do Synthetic Datasets Matter in?Handwritten Text Recognition?the most popular deep neural network architectures and presented a method based on autoencoder architecture and a schematic character generator. As a comparative model, we used a classifier trained on the whole NIST set of handwritten letters from the Latin alphabet. Our experiments showed that the

昏迷狀態(tài)

PCMO: Partial Classification from?CNN-Based Model Outputsnformation for making such predictions usually comes at the cost of retraining the model, changing the model architecture, or applying a new loss function. In an attempt to alleviate this computational burden, we fulfilled partial classification only based on pre-trained CNN-based model outputs (PCM

拱墻

Multi-branch Fusion Fully Convolutional Network for?Person Re-Identification, most of the existing methods adopt large CNN models as baseline, which is complicated and inefficient. In this paper, we propose an efficient and effective CNN architecture named Multi-branch Fusion Fully Convolutional Network (MBF-FCN). Firstly, multi-branch feature extractor module focusing on d

Culmination

ELUDE

EvoBA: An Evolution Strategy as a Strong Baseline for Black-Box Adversarial Attacksmble more the black-box adversarial conditions, lacking transparency and usually imposing natural, hard constraints on the query budget..We propose . (All the work is open source: . A full paper version is available at .), a black-box adversarial attack based on a surprisingly simple evolutionary se