Some visual recognition tasks are more challenging then the general ones as
they require professional categories of images. The previous efforts, like
fine-grained vision classification, primarily introduced models tailored to
specific tasks, like identifying bird species or car brands with limited
scalability and generalizability. This paper aims to design a scalable and
explainable model to solve Professional Visual Recognition tasks from a generic
standpoint. We introduce a biologically-inspired structure named Pro-NeXt and
reveal that Pro-NeXt exhibits substantial generalizability across diverse
professional fields such as fashion, medicine, and art-areas previously
considered disparate. Our basic-sized Pro-NeXt-B surpasses all preceding
task-specific models across 12 distinct datasets within 5 diverse domains.
Furthermore, we find its good scaling property that scaling up Pro-NeXt in
depth and width with increasing GFlops can consistently enhances its accuracy.
Beyond scalability and adaptability, the intermediate features of Pro-NeXt
achieve reliable object detection and segmentation performance without extra
training, highlighting its solid explainability. We will release the code to
foster further research in this area.

该论文介绍了一种可扩展且可解释的模型 Pro-NeXt，它在各种专业领域中展现了优秀的泛化性能，并具备可靠的物体检测和分割能力。

不仅仅是鸟和车：专业视觉识别的通用、可扩展和可解释模型

Not just Birds and Cars: Generic, Scalable and Explainable Models for  Professional Visual Recognition

Building scalable models to learn from diverse, multimodal data remains an
open challenge. For vision-language data, the dominant approaches are based on
contrastive learning objectives that train a separate encoder for each
modality. While effective, contrastive learning approaches introduce sampling
bias depending on the data augmentations used, which can degrade performance on
downstream tasks. Moreover, these methods are limited to paired image-text
data, and cannot leverage widely-available unpaired data. In this paper, we
investigate whether a large multimodal model trained purely via masked token
prediction, without using modality-specific encoders or contrastive learning,
can learn transferable representations for downstream tasks. We propose a
simple and scalable network architecture, the Multimodal Masked Autoencoder
(M3AE), which learns a unified encoder for both vision and language data via
masked token prediction. We provide an empirical study of M3AE trained on a
large-scale image-text dataset, and find that M3AE is able to learn
generalizable representations that transfer well to downstream tasks.
Surprisingly, we find that M3AE benefits from a higher text mask ratio
(50-90%), in contrast to BERT whose standard masking ratio is 15%, due to the
joint training of two data modalities. We also provide qualitative analysis
showing that the learned representation incorporates meaningful information
from both image and language. Lastly, we demonstrate the scalability of M3AE
with larger model size and training time, and its flexibility to train on both
paired image-text data as well as unpaired data.

本论文提出一种基于 Masked Token 预测的大型多模式模型 (M3AE)，能在不引入偏好于数据增强的对比学习目标的情况下，学习出适用于下游任务的可传递表示，并证明了高文本遮盖率下训练能够显著提升模型性能。M3AE 能够在配对和非配对的图像 - 文本数据上训练，具有可扩展性和灵活性。

多模态掩模自编码器学习可转移表示

Multimodal Masked Autoencoders Learn Transferable Representations

To scale Gaussian processes (GPs) to large data sets we introduce the robust
Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts
model for large-scale distributed GP regression. Unlike state-of-the-art sparse
GP approximations, the rBCM is conceptually simple and does not rely on
inducing or variational parameters. The key idea is to recursively distribute
computations to independent computational units and, subsequently, recombine
them to form an overall result. Efficient closed-form inference allows for
straightforward parallelisation and distributed computations with a small
memory footprint. The rBCM is independent of the computational graph and can be
used on heterogeneous computing infrastructures, ranging from laptops to
clusters. With sufficient computing resources our distributed GP model can
handle arbitrarily large data sets.

介绍了鲁棒贝叶斯委员会机 (rBCM)，是用于大规模分布式高斯过程回归的实用和可扩展的逐级分解模型。