Segment Anything Model (SAM) is a new foundation model that can be used as a
zero-shot object segmentation method with the use of either guide prompts such
as bounding boxes, polygons, or points. Alternatively, additional post
processing steps can be used to identify objects of interest after segmenting
everything in an image. Here we present a method using segment anything
together with a series of post processing steps to segment potato leaves,
called Leaf Only SAM. The advantage of this proposed method is that it does not
require any training data to produce its results so has many applications
across the field of plant phenotyping where there is limited high quality
annotated data available. We compare the performance of Leaf Only SAM to a Mask
R-CNN model which has been fine-tuned on our small novel potato leaf dataset.
On the evaluation dataset, Leaf Only SAM finds an average recall of 63.2 and an
average precision of 60.3, compared to recall of 78.7 and precision of 74.7 for
Mask R-CNN. Leaf Only SAM does not perform better than the fine-tuned Mask
R-CNN model on our data, but the SAM based model does not require any extra
training or annotation of our new dataset. This shows there is potential to use
SAM as a zero-shot classifier with the addition of post processing steps.

Segment Anything Model 是一种新的模型，可以用于零样本目标分割，可以使用边界框、多边形或点等提示进行模型训练，也可以使用后处理步骤，在植物表型学领域具有广泛的应用。在与 Mask R-CNN 模型的比较中，Leaf Only SAM 方法的召回率为 63.2，精确度为 60.3；相比之下，Mask R-CNN 的召回率为 78.7，精确度为 74.7。这表明使用后处理步骤的 SAM 模型可能具有零样本分类器的潜力，而无需训练新的数据集。

Leaf Only SAM: 一种用于零样本自动叶片分割的段落化任何东西流水线

Leaf Only SAM: A Segment Anything Pipeline for Zero-Shot Automated Leaf  Segmentation

Does a Variational AutoEncoder (VAE) consistently encode typical samples
generated from its decoder? This paper shows that the perhaps surprising answer
to this question is `No'; a (nominally trained) VAE does not necessarily
amortize inference for typical samples that it is capable of generating. We
study the implications of this behaviour on the learned representations and
also the consequences of fixing it by introducing a notion of self consistency.
Our approach hinges on an alternative construction of the variational
approximation distribution to the true posterior of an extended VAE model with
a Markov chain alternating between the encoder and the decoder. The method can
be used to train a VAE model from scratch or given an already trained VAE, it
can be run as a post processing step in an entirely self supervised way without
access to the original training data. Our experimental analysis reveals that
encoders trained with our self-consistency approach lead to representations
that are robust (insensitive) to perturbations in the input introduced by
adversarial attacks. We provide experimental results on the ColorMnist and
CelebA benchmark datasets that quantify the properties of the learned
representations and compare the approach with a baseline that is specifically
trained for the desired property.

本文证明了一种名义上训练过的变分自编码器（VAE）并不一定能够为其能够生成的典型样本便捷地进行推理，介绍了一种自一致性的概念来修正这种行为，并提出了一种基于马尔可夫链的变分近似分布的替代构造方法，具有自监督的后处理环节，可以使其生成的表示更加鲁棒，同时提供了 ColorMnist 和 CelebA 基准数据集上的实验结果。