Transductive few-shot learning algorithms have showed substantially superior
performance over their inductive counterparts by leveraging the unlabeled
queries. However, the vast majority of such methods are evaluated on perfectly
class-balanced benchmarks. It has been shown that they undergo remarkable drop
in performance under a more realistic, imbalanced setting. To this end, we
propose a novel algorithm to address imbalanced transductive few-shot learning,
named Adaptive Manifold. Our method exploits the underlying manifold of the
labeled support examples and unlabeled queries by using manifold similarity to
predict the class probability distribution per query. It is parameterized by
one centroid per class as well as a set of graph-specific parameters that
determine the manifold. All parameters are optimized through a loss function
that can be tuned towards class-balanced or imbalanced distributions. The
manifold similarity shows substantial improvement over Euclidean distance,
especially in the 1-shot setting. Our algorithm outperforms or is on par with
other state of the art methods in three benchmark datasets, namely
miniImageNet, tieredImageNet and CUB, and three different backbones, namely
ResNet-18, WideResNet-28-10 and DenseNet-121. In certain cases, our algorithm
outperforms the previous state of the art by as much as 4.2%.

提出了一种名为自适应流形的方法，用于解决在真实数据集不平衡环境下的转导式 few-shot 学习，该方法利用标记支持示例和无标签查询的底层流形来预测每个查询的类概率分布，并优化所有参数，表现出比其他最先进方法更好的性能。

不平衡传导式少样本学习的自适应流形

Adaptive manifold for imbalanced transductive few-shot learning

Image captioning applied to biomedical images can assist and accelerate the
diagnosis process followed by clinicians. This article is the first survey of
biomedical image captioning, discussing datasets, evaluation measures, and
state of the art methods. Additionally, we suggest two baselines, a weak and a
stronger one; the latter outperforms all current state of the art systems on
one of the datasets.

本文首次回顾讨论了医学图像字幕生成中的数据集、评估措施和现有技术，并提出了两种基准测试方法，其中强方法在一个数据集上的表现超出了所有现有技术系统。

生物医学图像字幕化调查

A Survey on Biomedical Image Captioning

Recent advances in deep domain adaptation reveal that adversarial learning
can be embedded into deep networks to learn transferable features that reduce
distribution discrepancy between the source and target domains. Existing domain
adversarial adaptation methods based on single domain discriminator only align
the source and target data distributions without exploiting the complex
multimode structures. In this paper, we present a multi-adversarial domain
adaptation (MADA) approach, which captures multimode structures to enable
fine-grained alignment of different data distributions based on multiple domain
discriminators. The adaptation can be achieved by stochastic gradient descent
with the gradients computed by back-propagation in linear-time. Empirical
evidence demonstrates that the proposed model outperforms state of the art
methods on standard domain adaptation datasets.

本文提出一种多重对抗领域自适应方法 (MADA)，能够捕获多模态结构，利用多个领域判别器实现不同数据分布的精细对齐，利用随机梯度下降计算梯度，证据表明该模型在标准领域自适应数据集上优于现有最先进方法。

多重对抗领域适应

Multi-Adversarial Domain Adaptation

In this work, we present an unconstrained face verification algorithm and
evaluate it on the recently released IJB-A dataset that aims to push the
boundaries of face verification methods. The proposed algorithm couples a deep
CNN-based approach with a low-dimensional discriminative embedding learnt using
triplet similarity constraints in a large margin fashion. Aside from yielding
performance improvement, this embedding provides significant advantages in
terms of memory and post-processing operations like hashing and visualization.
Experiments on the IJB-A dataset show that the proposed algorithm outperforms
state of the art methods in verification and identification metrics, while
requiring less training time.

提出了一种结合深度卷积神经网络和三元组相似性约束的低维判别嵌入的无约束人脸验证算法，在 IJB-A 数据集上显示出超越现有方法的性能提升和内存优化的优点。