Low-Light Video Enhancement (LLVE) has received considerable attention in
recent years. One of the critical requirements of LLVE is inter-frame
brightness consistency, which is essential for maintaining the temporal
coherence of the enhanced video. However, most existing single-image-based
methods fail to address this issue, resulting in flickering effect that
degrades the overall quality after enhancement. Moreover, 3D Convolution Neural
Network (CNN)-based methods, which are designed for video to maintain
inter-frame consistency, are computationally expensive, making them impractical
for real-time applications. To address these issues, we propose an efficient
pipeline named FastLLVE that leverages the Look-Up-Table (LUT) technique to
maintain inter-frame brightness consistency effectively. Specifically, we
design a learnable Intensity-Aware LUT (IA-LUT) module for adaptive
enhancement, which addresses the low-dynamic problem in low-light scenarios.
This enables FastLLVE to perform low-latency and low-complexity enhancement
operations while maintaining high-quality results. Experimental results on
benchmark datasets demonstrate that our method achieves the State-Of-The-Art
(SOTA) performance in terms of both image quality and inter-frame brightness
consistency. More importantly, our FastLLVE can process 1,080p videos at
$\mathit{50+}$ Frames Per Second (FPS), which is $\mathit{2 \times}$ faster
than SOTA CNN-based methods in inference time, making it a promising solution
for real-time applications. The code is available at
this https URL

提出了一种名为 FastLLVE 的高效管道，利用查找表（LUT）技术有效地保持帧间亮度一致性，从而实现低延迟和低复杂度的低光场景视频增强操作。

FastLLVE: 强度感知查找表实时低光视频增强

FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware  Lookup Table

In this paper, we consider the user positioning problem in the massive
multiple-input multiple-output (MIMO) orthogonal frequency-division
multiplexing (OFDM) system with a uniform planner antenna (UPA) array. Taking
advantage of the UPA array geometry and wide bandwidth, we advocate the use of
the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to
replace the traditional ones. The ADCPM embeds the stable and stationary
multipath characteristics, e.g. delay, power, and angle in the vertical and
horizontal directions, which are beneficial to positioning. Taking ADCPM
fingerprints as the inputs, we propose a novel three-dimensional (3D)
convolution neural network (CNN) enabled learning method to localize users' 3D
positions. In particular, such a 3D CNN model consists of a convolution
refinement module to refine the elementary feature maps from the ADCPM
fingerprints, three extended Inception modules to extract the advanced feature
maps, and a regression module to estimate the 3D positions. By intensive
simulations, the proposed 3D CNN-enabled positioning method is demonstrated to
achieve higher positioning accuracy than the traditional searching-based ones,
with reduced computational complexity and storage overhead, and the ADCPM
fingerprints are more robust to noise contamination.

本文提出一种利用角度 - 延时信道功率矩阵（ADCPM）作为指纹的用户定位方法，并应用三维卷积神经网络模型来提高位置估计准确度，模拟结果表明该方法比传统基于搜索的方法具有更高的定位准确度，更强的噪声抗扰性。

学习本地化：大规模 MIMO-OFDM 系统中用户定位的 3D CNN 方法

Learning to Localize: A 3D CNN Approach to User Positioning in Massive  MIMO-OFDM Systems

Recognizing facial expressions is one of the central problems in computer
vision. Temporal image sequences have useful spatio-temporal features for
recognizing expressions. In this paper, we propose a new 3D Convolution Neural
Network (CNN) that can be trained end-to-end for facial expression recognition
on temporal image sequences without using facial landmarks. More specifically,
a novel 3D convolutional layer that we call Local Binary Volume (LBV) layer is
proposed. The LBV layer, when used with our newly proposed LBVCNN network,
achieve comparable results compared to state-of-the-art landmark-based or
without landmark-based models on image sequences from CK+, Oulu-CASIA, and UNBC
McMaster shoulder pain datasets. Furthermore, our LBV layer reduces the number
of trainable parameters by a significant amount when compared to a conventional
3D convolutional layer. As a matter of fact, when compared to a 3x3x3
conventional 3D convolutional layer, the LBV layer uses 27 times less trainable
parameters.

本文提出了一种新的 3D 卷积神经网络，使用局部二进制体积层 (LBV) 来识别表情，可以在不使用面部标记的情况下进行训练，并在多个数据集中取得了与基于特征点或不基于特征点的模型相当的结果。此外，与传统的 3D 卷积层相比，LBV 层使用可训练参数的数量减少了 27 倍。