Despite the recent remarkable achievement in gaze estimation, efficient and
accurate personalization of gaze estimation without labels is a practical
problem but rarely touched on in the literature. To achieve efficient
personalization, we take inspiration from the recent advances in Natural
Language Processing (NLP) by updating a negligible number of parameters,
"prompts", at the test time. Specifically, the prompt is additionally attached
without perturbing original network and can contain less than 1% of a
ResNet-18's parameters. Our experiments show high efficiency of the prompt
tuning approach. The proposed one can be 10 times faster in terms of adaptation
speed than the methods compared. However, it is non-trivial to update the
prompt for personalized gaze estimation without labels. At the test time, it is
essential to ensure that the minimizing of particular unsupervised loss leads
to the goals of minimizing gaze estimation error. To address this difficulty,
we propose to meta-learn the prompt to ensure that its updates align with the
goal. Our experiments show that the meta-learned prompt can be effectively
adapted even with a simple symmetry loss. In addition, we experiment on four
cross-dataset validations to show the remarkable advantages of the proposed
method.

通过采用自然语言处理 (NLP) 的最新进展来实现高效个性化目光估计，同时在测试时通过较少数量的参数更新来保证迅速的适应速度，并通过元学习来确保更新与目标一致。

使用元提示的测试时个性化目光估计

Test-Time Personalization with Meta Prompt for Gaze Estimation

Meta Reinforcement Learning (Meta RL) trains agents that adapt to
fast-changing environments and tasks. Current strategies often lose adaption
efficiency due to the passive nature of model exploration, causing delayed
understanding of new transition dynamics. This results in particularly
fast-evolving tasks being impossible to solve. We propose a novel approach,
Hypothesis Network Planned Exploration (HyPE), that integrates an active and
planned exploration process via the hypothesis network to optimize adaptation
speed. HyPE uses a generative hypothesis network to form potential models of
state transition dynamics, then eliminates incorrect models through
strategically devised experiments. Evaluated on a symbolic version of the
Alchemy game, HyPE outpaces baseline methods in adaptation speed and model
accuracy, validating its potential in enhancing reinforcement learning
adaptation in rapidly evolving settings.

Meta Reinforcement Learning 的 Hypothesis Network Planned Exploration（HyPE）方法结合了主动和计划的探索过程，通过假设网络优化了适应速度，在快速演化的环境中显示出比基线方法更高的适应速度和模型准确性。

假设网络计划探索快速元强化学习适应

Hypothesis Network Planned Exploration for Rapid Meta-Reinforcement  Learning Adaptation

Few-shot object detection (FSOD), which aims at learning a generic detector
that can adapt to unseen tasks with scarce training samples, has witnessed
consistent improvement recently. However, most existing methods ignore the
efficiency issues, e.g., high computational complexity and slow adaptation
speed. Notably, efficiency has become an increasingly important evaluation
metric for few-shot techniques due to an emerging trend toward embedded AI. To
this end, we present an efficient pretrain-transfer framework (PTF) baseline
with no computational increment, which achieves comparable results with
previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an
initializer named knowledge inheritance (KI) to reliably initialize the novel
weights for the box classifier, which effectively facilitates the knowledge
transfer process and boosts the adaptation speed. Within the KI initializer, we
propose an adaptive length re-scaling (ALR) strategy to alleviate the vector
length inconsistency between the predicted novel weights and the pretrained
base weights. Finally, our approach not only achieves the SOTA results across
three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits
high efficiency with 1.8-100x faster adaptation speed against the other methods
on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is
the first work to consider the efficiency problem in FSOD. We hope to motivate
a trend toward powerful yet efficient few-shot technique development. The codes
are publicly available at this https URL.

通过提出一种高效的预训练迁移框架（PTF）基准线，采用 “知识传承”（KI）初始化程序来可靠地初始化盒子分类器的新权重，有效促进知识传递过程，并提高适应速度，该方法不仅在三个公共基准测试中取得了 SOTA 的结果，即 PASCAL VOC，COCO 和 LVIS，而且在少样本迁移过程中，在 COCO / LVIS 基准测试中比其他方法的适应速度快 1.8-100 倍，是第一个在 FSOD 中考虑效率问题的研究。

知识继承下的高效少样本目标检测

Efficient Few-Shot Object Detection via Knowledge Inheritance

Consider a collection of datasets generated by unknown interventions on an
unknown structural causal model $G$. Recently, Bengio et al. (2020) conjectured
that among all candidate models, $G$ is the fastest to adapt from one dataset
to another, along with promising experiments. Indeed, intuitively $G$ has less
mechanisms to adapt, but this justification is incomplete. Our contribution is
a more thorough analysis of this hypothesis. We investigate the adaptation
speed of cause-effect SCMs. Using convergence rates from stochastic
optimization, we justify that a relevant proxy for adaptation speed is distance
in parameter space after intervention. Applying this proxy to categorical and
normal cause-effect models, we show two results. When the intervention is on
the cause variable, the SCM with the correct causal direction is advantaged by
a large factor. When the intervention is on the effect variable, we
characterize the relative adaptation speed. Surprisingly, we find situations
where the anticausal model is advantaged, falsifying the initial hypothesis.
Code to reproduce experiments is available at
this https URL

本文在未知的结构因果模型 G 执行未知干预收集的数据集的基础上，使用随机优化的收敛速率，对因果关系下的结构因果模型 (SCMs) 适应速度进行了研究，并利用参数空间距离作为代理来衡量适应速度。我们发现，当干预在原因变量上时，SCM 的正确因果方向会得到巨大优势，而当干预在效应变量上时，我们对其相对适应速度进行了表征。令人惊讶的是，我们发现反因果模型在某些情况下具有优势，从而推翻初始假设。