Referring video object segmentation (RVOS) is a task that aims to segment the
target object in all video frames based on a sentence describing the object.
Previous RVOS methods have achieved significant performance with
densely-annotated datasets, whose construction is expensive and time-consuming.
To relieve the burden of data annotation while maintaining sufficient
supervision for segmentation, we propose a new annotation scheme, in which we
label the frame where the object first appears with a mask and use bounding
boxes for the subsequent frames. Based on this scheme, we propose a method to
learn from this weak annotation. Specifically, we design a cross frame
segmentation method, which uses the language-guided dynamic filters to
thoroughly leverage the valuable mask annotation and bounding boxes. We further
develop a bi-level contrastive learning method to encourage the model to learn
discriminative representation at the pixel level. Extensive experiments and
ablative analyses show that our method is able to achieve competitive
performance without the demand of dense mask annotation. The code will be
available at this https URL

基于一种新的注释方案，我们提出了一种弱监督的方法，通过语言引导的动态过滤器和双层对比学习方法，实现了在不要求密集标注的情况下取得了有竞争力的性能。

从弱监督的注释中学习视频物体指代分割

Learning Referring Video Object Segmentation from Weak Annotation

Traditional semi-supervised learning tasks assume that both labeled and
unlabeled data follow the same class distribution, but the realistic open-world
scenarios are of more complexity with unknown novel classes mixed in the
unlabeled set. Therefore, it is of great challenge to not only recognize
samples from known classes but also discover the unknown number of novel
classes within the unlabeled data. In this paper, we introduce a new open-world
semi-supervised novel class discovery approach named OpenNCD, a progressive
bi-level contrastive learning method over multiple prototypes. The proposed
method is composed of two reciprocally enhanced parts. First, a bi-level
contrastive learning method is introduced, which maintains the pair-wise
similarity of the prototypes and the prototype group levels for better
representation learning. Then, a reliable prototype similarity metric is
proposed based on the common representing instances. Prototypes with high
similarities will be grouped progressively for known class recognition and
novel class discovery. Extensive experiments on three image datasets are
conducted and the results show the effectiveness of the proposed method in
open-world scenarios, especially with scarce known classes and labels.

该文提出了一种名为 OpenNCD 的新的半监督开放世界的未知类别发现方法，它是一种在多个原型上进行的渐进式双层对比学习方法，该方法由两个相互增强的部分组成。首先，介绍了一种双层对比学习方法，然后提出了一个可靠的原型相似度度量方法。对三个图像数据集进行了广泛的实验，结果表明该方法在开放世界的情况下特别有效，特别是在已知类别和标签稀缺的情况下。

开放式半监督新类别发现

Open-world Semi-supervised Novel Class Discovery

Conversation disentanglement aims to group utterances into detached sessions,
which is a fundamental task in processing multi-party conversations. Existing
methods have two main drawbacks. First, they overemphasize pairwise utterance
relations but pay inadequate attention to the utterance-to-context relation
modeling. Second, huge amount of human annotated data is required for training,
which is expensive to obtain in practice. To address these issues, we propose a
general disentangle model based on bi-level contrastive learning. It brings
closer utterances in the same session while encourages each utterance to be
near its clustered session prototypes in the representation space. Unlike
existing approaches, our disentangle model works in both supervised setting
with labeled data and unsupervised setting when no such data is available. The
proposed method achieves new state-of-the-art performance on both settings
across several public datasets.

该研究提出了一种基于双层对比学习的通话分离模型，旨在处理多方对话中的基本任务，同时在有监督和无监督学习的设置下，均可取得最先进的性能表现。