CLIP proved that aligning visual and language spaces is key to solving many
vision tasks without explicit training, but required to train image and text
encoders from scratch on a huge dataset. LiT improved this by only training the
text encoder and using a pre-trained vision network. In this paper, we show
that a common space can be created without any training at all, using
single-domain encoders (trained with or without supervision) and a much smaller
amount of image-text pairs. Furthermore, our model has unique properties. Most
notably, deploying a new version with updated training samples can be done in a
matter of seconds. Additionally, the representations in the common space are
easily interpretable as every dimension corresponds to the similarity of the
input to a unique entry in the multimodal dataset. Experiments on standard
zero-shot visual benchmarks demonstrate the typical transfer ability of
image-text models. Overall, our method represents a simple yet surprisingly
strong baseline for foundation multi-modal models, raising important questions
on their data efficiency and on the role of retrieval in machine learning.

本文通过使用单领域编码器和较少的图文对，证明可以在不进行任何训练的情况下创建一个共同的空间，并且该模型对于基于图文模型的转移效果很好。

ASIF: 将耦合数据转换为多模态无需训练的单一模态模型

ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training

This paper presents contrastive-tuning, a simple method employing contrastive
training to align image and text models while still taking advantage of their
pre-training. In our empirical study we find that locked pre-trained image
models with unlocked text models work best. We call this instance of
contrastive-tuning "Locked-image Tuning" (LiT), which just teaches a text model
to read out good representations from a pre-trained image model for new tasks.
A LiT model gains the capability of zero-shot transfer to new vision tasks,
such as image classification or retrieval. The proposed LiT is widely
applicable; it works reliably with multiple pre-training methods (supervised
and unsupervised) and across diverse architectures (ResNet, Vision Transformers
and MLP-Mixer) using three different image-text datasets. With the
transformer-based pre-trained ViT-g/14 model, the LiT model achieves 85.2%
zero-shot transfer accuracy on the ImageNet test set, and 82.5% on the
challenging out-of-distribution ObjectNet test set.

该研究提出了对比度调整（contrastive-tuning）方法，通过对比训练使图像和文本模型对齐，该方法能够在使用预训练的同时仍然发挥其优势，其中最佳表现的是锁定的预先训练的图像模型和解锁的文本模型。LiT 模型具有其零 - shot 迁移能力，能够迁移新的视觉任务，本研究在多个预训练方法（有监督和无监督）以及不同架构 (ResNet, Vision Transformers 和 MLP-Mixer) 的三个不同的图像－文本数据集上得到了可靠的效果。

LiT: 基于锁定图像文本调整的零次迁移

LiT: Zero-Shot Transfer with Locked-image text Tuning

With the increasing availability of traffic data and advance of deep
reinforcement learning techniques, there is an emerging trend of employing
reinforcement learning (RL) for traffic signal control. A key question for
applying RL to traffic signal control is how to define the reward and state.
The ultimate objective in traffic signal control is to minimize the travel
time, which is difficult to reach directly. Hence, existing studies often
define reward as an ad-hoc weighted linear combination of several traffic
measures. However, there is no guarantee that the travel time will be optimized
with the reward. In addition, recent RL approaches use more complicated state
(e.g., image) in order to describe the full traffic situation. However, none of
the existing studies has discussed whether such a complex state representation
is necessary. This extra complexity may lead to significantly slower learning
process but may not necessarily bring significant performance gain.
In this paper, we propose to re-examine the RL approaches through the lens of
classic transportation theory. We ask the following questions: (1) How should
we design the reward so that one can guarantee to minimize the travel time? (2)
How to design a state representation which is concise yet sufficient to obtain
the optimal solution? Our proposed method LIT is theoretically supported by the
classic traffic signal control methods in transportation field. LIT has a very
simple state and reward design, thus can serve as a building block for future
RL approaches to traffic signal control. Extensive experiments on both
synthetic and real datasets show that our method significantly outperforms the
state-of-the-art traffic signal control methods.

本文通过经典交通理论的视角重新审视强化学习在交通信号控制中的应用，提出了 LIT 方法，使用简单的状态和奖励设计达到最优解，实验结果显示该方法明显优于现有交通信号控制方法。