Scene text synthesis involves rendering specified texts onto arbitrary
images. Current methods typically formulate this task in an end-to-end manner
but lack effective character-level guidance during training. Besides, their
text encoders, pre-trained on a single font type, struggle to adapt to the
diverse font styles encountered in practical applications. Consequently, these
methods suffer from character distortion, repetition, and absence, particularly
in polystylistic scenarios. To this end, this paper proposes DreamText for
high-fidelity scene text synthesis. Our key idea is to reconstruct the
diffusion training process, introducing more refined guidance tailored to this
task, to expose and rectify the model's attention at the character level and
strengthen its learning of text regions. This transformation poses a hybrid
optimization challenge, involving both discrete and continuous variables. To
effectively tackle this challenge, we employ a heuristic alternate optimization
strategy. Meanwhile, we jointly train the text encoder and generator to
comprehensively learn and utilize the diverse font present in the training
dataset. This joint training is seamlessly integrated into the alternate
optimization process, fostering a synergistic relationship between learning
character embedding and re-estimating character attention. Specifically, in
each step, we first encode potential character-generated position information
from cross-attention maps into latent character masks. These masks are then
utilized to update the representation of specific characters in the current
step, which, in turn, enables the generator to correct the character's
attention in the subsequent steps. Both qualitative and quantitative results
demonstrate the superiority of our method to the state of the art.

本研究提出了 DreamText 方法，旨在通过增加细化的字符级指导以及多样的字体训练，提高高保真场景文本合成效果，并通过融合离散和连续变量的混合优化策略来解决该优化问题。实验结果表明，本方法在质量和数量上优于现有方法。

高保真场景文本合成

High Fidelity Scene Text Synthesis

Mixed-precision quantization mostly predetermines the model bit-width
settings before actual training due to the non-differential bit-width sampling
process, obtaining sub-optimal performance. Worse still, the conventional
static quality-consistent training setting, i.e., all data is assumed to be of
the same quality across training and inference, overlooks data quality changes
in real-world applications which may lead to poor robustness of the quantized
models. In this paper, we propose a novel Data Quality-aware Mixed-precision
Quantization framework, dubbed DQMQ, to dynamically adapt quantization
bit-widths to different data qualities. The adaption is based on a bit-width
decision policy that can be learned jointly with the quantization training.
Concretely, DQMQ is modeled as a hybrid reinforcement learning (RL) task that
combines model-based policy optimization with supervised quantization training.
By relaxing the discrete bit-width sampling to a continuous probability
distribution that is encoded with few learnable parameters, DQMQ is
differentiable and can be directly optimized end-to-end with a hybrid
optimization target considering both task performance and quantization
benefits. Trained on mixed-quality image datasets, DQMQ can implicitly select
the most proper bit-width for each layer when facing uneven input qualities.
Extensive experiments on various benchmark datasets and networks demonstrate
the superiority of DQMQ against existing fixed/mixed-precision quantization
methods.

本研究提出了一种名为 DQMQ 的新型混合精度量化框架，可动态地适应不同数据质量，通过学习一个决策规则，它被建模为一种混合强化学习任务，该任务结合了基于模型的策略优化和监督量化训练。通过在混合质量的图像数据集上进行训练，DQMQ 可以在面对不均匀输入质量时隐式地选择每个层的最适宜比特宽度，在各种基准数据集和网络上的大量实验表明，DQMQ 比现有的固定 / 混合精度量化方法更为优越。

基于混合强化学习的数据质量感知混合精度量化

Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning

PARAFAC2 has demonstrated success in modeling irregular tensors, where the
tensor dimensions vary across one of the modes. An example scenario is modeling
treatments across a set of patients with the varying number of medical
encounters over time. Despite recent improvements on unconstrained PARAFAC2,
its model factors are usually dense and sensitive to noise which limits their
interpretability. As a result, the following open challenges remain: a) various
modeling constraints, such as temporal smoothness, sparsity and non-negativity,
are needed to be imposed for interpretable temporal modeling and b) a scalable
approach is required to support those constraints efficiently for large
datasets. To tackle these challenges, we propose a {\it CO}nstrained {\it
PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints
such as temporal smoothness, sparsity, and non-negativity in the resulting
factors. To efficiently support all those constraints, COPA adopts a hybrid
optimization framework using alternating optimization and alternating direction
method of multiplier (AO-ADMM). As evaluated on large electronic health record
(EHR) datasets with hundreds of thousands of patients, COPA achieves
significant speedups (up to 36 times faster) over prior PARAFAC2 approaches
that only attempt to handle a subset of the constraints that COPA enables.
Overall, our method outperforms all the baselines attempting to handle a subset
of the constraints in terms of speed, while achieving the same level of
accuracy. Through a case study on temporal phenotyping of medically complex
children, we demonstrate how the constraints imposed by COPA reveal concise
phenotypes and meaningful temporal profiles of patients. The clinical
interpretation of both the phenotypes and the temporal profiles was confirmed
by a medical expert.

本研究提出了一种基于协同优化和单向距离方法（AO-ADMM）的新的 COPA 方法，该方法的目的是解决 PARAFAC2 模型的因子分解在应对时空约束时的可解释性不足和噪声敏感性等问题。通过实验，COA 方法比以前的 PARAFAC2 方法更快，并取得了与在精度方面与前者相同的表现。