Abstract We present the Recognize Anything Model (RAM): a strong foundation model for
image tagging. RAM makes a substantial step for foundation models in computer vision …

This paper presents Tag2Text, a vision language pre-training (VLP) framework, which
introduces image tagging into vision-language models to guide the learning of visual …

Insect pests pose a significant and increasing threat to agricultural production worldwide.
However, most existing recognition methods are built upon well-known convolutional neural …

Many real-world image recognition problems, such as diagnostic medical imaging exams,
are “long-tailed”–there are a few common findings followed by many more relatively rare …

Existing text-to-image generation approaches have set high standards for photorealism and
text-image correspondence, largely benefiting from web-scale text-image datasets, which …

Large-scale multi-label classification datasets are commonly, and perhaps inevitably,
partially annotated. That is, only a small subset of labels are annotated per sample. Different …

This paper presents the FlowTransformer framework, a novel approach for implementing
transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer …

Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to
process them with an identical structure. One main obstacle lies in the high-dimensional …

In single positive multi-label learning (SPML), only one of multiple positive labels is
observed for each instance. The previous work trains the model by simply treating …

Text-to-Image generation models have revolutionized the artwork design process and
enabled anyone to create high-quality images by entering text descriptions called prompts …