AI democratization aims to create a world in which the average person can
utilize AI techniques. To achieve this goal, numerous research institutes have
attempted to make their results accessible to the public. In particular, large
pre-trained models trained on large-scale data have shown unprecedented
potential, and their release has had a significant impact. However, most of the
released models specialize in the English language, and thus, AI
democratization in non-English-speaking communities is lagging significantly.
To reduce this gap in AI access, we released Generative Pre-trained Transformer
(GPT), Contrastive Language and Image Pre-training (CLIP), Stable Diffusion,
and Hidden-unit Bidirectional Encoder Representations from Transformers
(HuBERT) pre-trained in Japanese. By providing these models, users can freely
interface with AI that aligns with Japanese cultural values and ensures the
identity of Japanese culture, thus enhancing the democratization of AI.
Additionally, experiments showed that pre-trained models specialized for
Japanese can efficiently achieve high performance in Japanese tasks.

通过提供在日本文化价值观中与之一致的人工智能模型，扩大了人工智能的民主化，同时实验证明专门用于日本的预训练模型能够高效地在日本任务上取得高性能。

日语预训练模型发布

Release of Pre-Trained Models for the Japanese Language

Biologically inspired spiking neural networks (SNNs) have garnered
considerable attention due to their low-energy consumption and spatio-temporal
information processing capabilities. Most existing SNNs training methods first
integrate output information across time steps, then adopt the cross-entropy
(CE) loss to supervise the prediction of the average representations. However,
in this work, we find the method above is not ideal for the SNNs training as it
omits the temporal dynamics of SNNs and degrades the performance quickly with
the decrease of inference time steps. One tempting method to model temporal
correlations is to apply the same label supervision at each time step and treat
them identically. Although it can acquire relatively consistent performance
across various time steps, it still faces challenges in obtaining SNNs with
high performance. Inspired by these observations, we propose Temporal-domain
supervised Contrastive Learning (TCL) framework, a novel method to obtain SNNs
with low latency and high performance by incorporating contrastive supervision
with temporal domain information. Contrastive learning (CL) prompts the network
to discern both consistency and variability in the representation space,
enabling it to better learn discriminative and generalizable features. We
extend this concept to the temporal domain of SNNs, allowing us to flexibly and
fully leverage the correlation between representations at different time steps.
Furthermore, we propose a Siamese Temporal-domain supervised Contrastive
Learning (STCL) framework to enhance the SNNs via augmentation, temporal and
class constraints simultaneously. Extensive experimental results demonstrate
that SNNs trained by our TCL and STCL can achieve both high performance and low
latency, achieving state-of-the-art performance on a variety of datasets (e.g.,
CIFAR-10, CIFAR-100, and DVS-CIFAR10).

本研究提出了一种时域监督对比学习的方法，可以使神经网络在处理时间序列信息时具备低延迟和高性能，取得了 CIFAR-10、CIFAR-100 和 DVS-CIFAR10 等数据集的最新性能水平。

脉冲神经网络的时间对比学习

Temporal Contrastive Learning for Spiking Neural Networks

Image enhancement is a subjective process whose targets vary with user
preferences. In this paper, we propose a deep learning-based image enhancement
method covering multiple tonal styles using only a single model dubbed
StarEnhancer. It can transform an image from one tonal style to another, even
if that style is unseen. With a simple one-time setting, users can customize
the model to make the enhanced images more in line with their aesthetics. To
make the method more practical, we propose a well-designed enhancer that can
process a 4K-resolution image over 200 FPS but surpasses the contemporaneous
single style image enhancement methods in terms of PSNR, SSIM, and LPIPS.
Finally, our proposed enhancement method has good interactability, which allows
the user to fine-tune the enhanced image using intuitive options.

本研究提出了一种基于深度学习的图像增强方法，使用单一模型 StarEnhancer 覆盖多种色调样式，可将图像从一种色调样式转换为另一种，具有高时效性和优秀的交互性能。

StarEnhancer：实时和风格感知的图像增强学习

StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement

Transformer, first applied to the field of natural language processing, is a
type of deep neural network mainly based on the self-attention mechanism.
Thanks to its strong representation capabilities, researchers are looking at
ways to apply transformer to computer vision tasks. In a variety of visual
benchmarks, transformer-based models perform similar to or better than other
types of networks such as convolutional and recurrent neural networks. Given
its high performance and less need for vision-specific inductive bias,
transformer is receiving more and more attention from the computer vision
community. In this paper, we review these vision transformer models by
categorizing them in different tasks and analyzing their advantages and
disadvantages. The main categories we explore include the backbone network,
high/mid-level vision, low-level vision, and video processing. We also include
efficient transformer methods for pushing transformer into real device-based
applications. Furthermore, we also take a brief look at the self-attention
mechanism in computer vision, as it is the base component in transformer.
Toward the end of this paper, we discuss the challenges and provide several
further research directions for vision transformers.

本文回顾了将 Transformer 应用于计算机视觉任务中的视觉 Transformer 模型，并分为不同任务类别，分析它们的优点和缺点，同时也介绍了将 Transformer 应用到实际设备应用的有效方法。最后，本文还探讨了计算机视觉中的自注意机制以及视觉 Transformer 面临的挑战及进一步研究方向。