Humans constantly interact with their surrounding environments. Current
human-centric generative models mainly focus on synthesizing humans plausibly
interacting with static scenes and objects, while the dynamic human
action-reaction synthesis for ubiquitous causal human-human interactions is
less explored. Human-human interactions can be regarded as asymmetric with
actors and reactors in atomic interaction periods. In this paper, we
comprehensively analyze the asymmetric, dynamic, synchronous, and detailed
nature of human-human interactions and propose the first multi-setting human
action-reaction synthesis benchmark to generate human reactions conditioned on
given human actions. To begin with, we propose to annotate the actor-reactor
order of the interaction sequences for the NTU120, InterHuman, and Chi3D
datasets. Based on them, a diffusion-based generative model with a Transformer
decoder architecture called ReGenNet together with an explicit distance-based
interaction loss is proposed to predict human reactions in an online manner,
where the future states of actors are unavailable to reactors. Quantitative and
qualitative results show that our method can generate instant and plausible
human reactions compared to the baselines, and can generalize to unseen actor
motions and viewpoint changes.

提出了一种异步、动态、同步和详细的人 - 人互动综合基准方法，利用 ReGenNet 模型和 Transformer 解码器体系结构生成人的反应，结果显示我们的方法可以产生即时且可信的人的反应。

ReGenNet：走向人类行为反应综合

ReGenNet: Towards Human Action-Reaction Synthesis

Transformers have emerged as the prevailing standard solution for various AI
tasks, including computer vision and natural language processing. The widely
adopted Query, Key, and Value formulation (QKV) has played a significant role
in this. Nevertheless, no research has examined the essentiality of these three
components for transformer performance. Therefore, we conducted an evaluation
of the key-value formulation (KV), which generates symmetric attention maps,
along with an asymmetric version that incorporates a 2D positional encoding
into the attention matrix. Remarkably, this transformer requires fewer
parameters and computation than the original one. Through experiments
encompassing three task types -- synthetics (such as reversing or sorting a
list), vision (mnist or cifar classification), and NLP (character generation
and translation) -- we discovered that the KV transformer occasionally
outperforms the QKV transformer. However, it also exhibits instances of
underperformance compared to QKV, making it challenging to draw a definitive
conclusion. Nonetheless, we consider the reported results to be encouraging and
anticipate that they may pave the way for more efficient transformers in the
future.

通过对 QKV 模式性能进行评估，我们发现一个结合了 2D 位置编码的不对称的 KV 变压器常常比 QKV 变压器更有效且需要更少的参数和计算。

键值变换器

Key-Value Transformer

Convolution is spatially-symmetric, i.e., the visual features are independent
of its position in the image, which limits its ability to utilize contextual
cues for visual recognition. This paper addresses this issue by introducing a
recalibration process, which refers to the surrounding region of each neuron,
computes an importance value and multiplies it to the original neural response.
Our approach is named multi-scale spatially-asymmetric recalibration (MS-SAR),
which extracts visual cues from surrounding regions at multiple scales, and
designs a weighting scheme which is asymmetric in the spatial domain. MS-SAR is
implemented in an efficient way, so that only small fractions of extra
parameters and computations are required. We apply MS-SAR to several popular
building blocks, including the residual block and the densely-connected block,
and demonstrate its superior performance in both CIFAR and ILSVRC2012
classification tasks.

本文提出了一种名为多尺度空间非对称重新校准的方法，在残差块和密集连接块等多个流行的模块上实现，并证明其在 CIFAR 和 ILSVRC2012 分类任务中具有优越的性能表现，通过从多个尺度提取周围区域的信息，不对称的空间加权方案来提高卷积能够利用上下文提示的能力。

多尺度空间非对称再校准图像分类

Multi-Scale Spatially-Asymmetric Recalibration for Image Classification

We study the convex relaxation of clustering and hamming embedding, focusing
on the asymmetric case (co-clustering and asymmetric hamming embedding),
understanding their relationship to LSH as studied by (Charikar 2002) and to
the max-norm ball, and the differences between their symmetric and asymmetric
versions.

该研究聚焦于非对称情况下（共聚类和非对称哈希嵌入），探究其与局部敏感哈希（Charikar 2002）和 max-norm 球的关系以及对称和非对称版本之间的差异，研究聚类和哈希嵌入的凸松弛问题。