Continual learning (CL) with Vision-Language Models (VLMs) has overcome the
constraints of traditional CL, which only focuses on previously encountered
classes. During the CL of VLMs, we need not only to prevent the catastrophic
forgetting on incrementally learned knowledge but also to preserve the
zero-shot ability of VLMs. However, existing methods require additional
reference datasets to maintain such zero-shot ability and rely on
domain-identity hints to classify images across different domains. In this
study, we propose Regression-based Analytic Incremental Learning (RAIL), which
utilizes a recursive ridge regression-based adapter to learn from a sequence of
domains in a non-forgetting manner and decouple the cross-domain correlations
by projecting features to a higher-dimensional space. Cooperating with a
training-free fusion module, RAIL absolutely preserves the VLM's zero-shot
ability on unseen domains without any reference data. Additionally, we
introduce Cross-domain Task-Agnostic Incremental Learning (X-TAIL) setting. In
this setting, a CL learner is required to incrementally learn from multiple
domains and classify test images from both seen and unseen domains without any
domain-identity hint. We theoretically prove RAIL's absolute memorization on
incrementally learned domains. Experiment results affirm RAIL's
state-of-the-art performance in both X-TAIL and existing Multi-domain
Task-Incremental Learning settings. The code will be released upon acceptance.

利用回归分析的增量学习和跨领域任务无关增量学习方法，保持视觉 - 语言模型的零 - shot 能力和在多领域下的性能。

推进视觉语言模型的领域间辨识性在连续学习中

Advancing Cross-domain Discriminability in Continual Learning of  Vison-Language Models

Segmentation foundation models have attracted great interest, however, none
of them are adequate enough for the use cases in 3D computed tomography scans
(CT) images. Existing works finetune on medical images with 2D foundation
models trained on natural images, but interactive segmentation, especially in
2D, is too time-consuming for 3D scans and less useful for large cohort
analysis. Models that can perform out-of-the-box automatic segmentation are
more desirable. However, the model trained in this way lacks the ability to
perform segmentation on unseen objects like novel tumors. Thus for 3D medical
image analysis, an ideal segmentation solution might expect two features:
accurate out-of-the-box performance covering major organ classes, and effective
adaptation or zero-shot ability to novel structures. In this paper, we discuss
what features a 3D CT segmentation foundation model should have, and introduce
VISTA3D, Versatile Imaging SegmenTation and Annotation model. The model is
trained systematically on 11454 volumes encompassing 127 types of human
anatomical structures and various lesions and provides accurate out-of-the-box
segmentation. The model's design also achieves state-of-the-art zero-shot
interactive segmentation in 3D. The novel model design and training recipe
represent a promising step toward developing a versatile medical image
foundation model. Code and model weights will be released shortly. The early
version of online demo can be tried on
this https URL

3D CT 分割基础模型应具备一次准确性能和自适应能力，而 VISTA3D 模型在系统训练时能够提供准确的立即分割，并实现了最先进的三维零交互分割。

VISTA3D：一种用于 3D 计算机断层扫描的多功能影像分割和注释模型

VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D  Computed Tomography

Referring image segmentation (RIS) aims to locate the particular region
corresponding to the language expression. Existing methods incorporate features
from different modalities in a \emph{bottom-up} manner. This design may get
some unnecessary image-text pairs, which leads to an inaccurate segmentation
mask. In this paper, we propose a referring image segmentation method called
HARIS, which introduces the Human-Like Attention mechanism and uses the
parameter-efficient fine-tuning (PEFT) framework. To be specific, the
Human-Like Attention gets a \emph{feedback} signal from multi-modal features,
which makes the network center on the specific objects and discard the
irrelevant image-text pairs. Besides, we introduce the PEFT framework to
preserve the zero-shot ability of pre-trained encoders. Extensive experiments
on three widely used RIS benchmarks and the PhraseCut dataset demonstrate that
our method achieves state-of-the-art performance and great zero-shot ability.

本文提出了一种称为 HARIS 的指代图像分割方法，引入了类似于人类的注意机制和参数高效微调框架，通过多模态特征的反馈信号使网络专注于特定对象并丢弃无关的图像 - 文本对，同时引入了参数高效微调框架以保持预训练编码器的零样本能力，实验证明该方法在三个广泛使用的 RIS 基准和 PhraseCut 数据集上取得了最先进的性能和出色的零样本能力。

HARIS：用于参考图像分割的类人注意力

HARIS: Human-Like Attention for Reference Image Segmentation

Product attribute value extraction is an important task in e-Commerce which
can help several downstream applications such as product search and
recommendation. Most previous models handle this task using sequence labeling
or question answering method which rely on the sequential position information
of values in the product text and are vulnerable to data discrepancy between
training and testing. This limits their generalization ability to real-world
scenario in which each product can have multiple descriptions across various
shopping platforms with different composition of text and style. They also have
limited zero-shot ability to new values. In this paper, we propose a multi-task
learning model with value generation/classification and attribute prediction
called JPAVE to predict values without the necessity of position information of
values in the text. Furthermore, the copy mechanism in value generator and the
value attention module in value classifier help our model address the data
discrepancy issue by only focusing on the relevant part of input text and
ignoring other information which causes the discrepancy issue such as sentence
structure in the text. Besides, two variants of our model are designed for
open-world and closed-world scenarios. In addition, copy mechanism introduced
in the first variant based on value generation can improve its zero-shot
ability for identifying unseen values. Experimental results on a public dataset
demonstrate the superiority of our model compared with strong baselines and its
generalization ability of predicting new values.

我们提出了一个多任务学习模型 JPAVE，通过值的生成 / 分类和属性预测来预测文本中的值，解决了数据差异问题和零样本能力的限制。