In recent years, pre-trained multimodal large models have attracted
widespread attention due to their outstanding performance in various multimodal
applications. Nonetheless, the extensive computational resources and vast
datasets required for their training present significant hurdles for deployment
in environments with limited computational resources. To address this
challenge, we propose a novel dynamic self-adaptive multiscale distillation
from pre-trained multimodal large model for efficient cross-modal
representation learning for the first time. Unlike existing distillation
methods, our strategy employs a multiscale perspective, enabling the extraction
structural knowledge across from the pre-trained multimodal large model.
Ensuring that the student model inherits a comprehensive and nuanced
understanding of the teacher knowledge. To optimize each distillation loss in a
balanced and efficient manner, we propose a dynamic self-adaptive distillation
loss balancer, a novel component eliminating the need for manual loss weight
adjustments and dynamically balances each loss item during the distillation
process. Our methodology streamlines pre-trained multimodal large models using
only their output features and original image-level information, requiring
minimal computational resources. This efficient approach is suited for various
applications and allows the deployment of advanced multimodal technologies even
in resource-limited settings. Extensive experiments has demonstrated that our
method maintains high performance while significantly reducing model complexity
and training costs. Moreover, our distilled student model utilizes only
image-level information to achieve state-of-the-art performance on cross-modal
retrieval tasks, surpassing previous methods that relied on region-level
information.

为了在计算资源有限的环境下部署高级多模态技术，我们提出了一种从预训练多模态大模型中动态自适应多尺度蒸馏的方法，通过仅使用输出特征和原始图像级信息来优化模型，显著降低复杂性和训练成本，实现了跨模态检索任务的最先进性能。

基于预训练多模态大模型的动态自适应多尺度蒸馏，用于高效的跨模态表示学习

Dynamic Self-adaptive Multiscale Distillation from Pre-trained  Multimodal Large Model for Efficient Cross-modal Representation Learning

The consumption of microbial-contaminated food and water is responsible for
the deaths of millions of people annually. Smartphone-based microscopy systems
are portable, low-cost, and more accessible alternatives for the detection of
Giardia and Cryptosporidium than traditional brightfield microscopes. However,
the images from smartphone microscopes are noisier and require manual cyst
identification by trained technicians, usually unavailable in resource-limited
settings. Automatic detection of (oo)cysts using deep-learning-based object
detection could offer a solution for this limitation. We evaluate the
performance of three state-of-the-art object detectors to detect (oo)cysts of
Giardia and Cryptosporidium on a custom dataset that includes both smartphone
and brightfield microscopic images from vegetable samples. Faster RCNN,
RetinaNet, and you only look once (YOLOv8s) deep-learning models were employed
to explore their efficacy and limitations. Our results show that while the
deep-learning models perform better with the brightfield microscopy image
dataset than the smartphone microscopy image dataset, the smartphone microscopy
predictions are still comparable to the prediction performance of non-experts.

通过深度学习目标检测自动识别 Giardia 和 Cryptosporidium，可以替代传统显微镜检查，并在资源匮乏的地区提高检测效率。

智能手机显微图像辅助检测和定量分析贾第鞭毛虫和隐孢子

Deep-learning assisted detection and quantification of (oo)cysts of  Giardia and Cryptosporidium on smartphone microscopy images

Word embeddings are reliable feature representations of words used to obtain
high quality results for various NLP applications. Uncontextualized word
embeddings are used in many NLP tasks today, especially in resource-limited
settings where high memory capacity and GPUs are not available. Given the
historical success of word embeddings in NLP, we propose a retrospective on
some of the most well-known word embedding algorithms. In this work, we
deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of
the common conditions that seem to be required for making performant word
embeddings. We believe that the theoretical findings in this paper can provide
a basis for more informed development of future models.

本文针对词嵌入在自然语言处理中的应用，通过对 Word2vec、GloVe 等模型进行剖析，提出了一种通用形式，揭示出制作词嵌入所需的共同条件，并强调理论研究对未来模型开发的指导作用。