Solving multi-label recognition (MLR) for images in the low-label regime is a challenging
task with many real-world applications. Recent work learns an alignment between textual …

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-
trained models (eg CLIP) to various downstream tasks in data-limited or label-limited …

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive
to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to …

Multi-label image recognition with partial labels (MLR-PL), in which some labels are known
while others are unknown for each image, may greatly reduce the cost of annotation and …

Due to the expensive costs of collecting labels in multi-label classification datasets, partially
annotated multi-label classification has become an emerging field in computer vision. One …

Multi-label image recognition in the low-label regime is a task of great challenge and
practical significance. Previous works have focused on learning the alignment between …

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 …

Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer
their relationships for a given video. It requires not only a comprehensive understanding of …

Partially annotated images are easy to obtain in multi-label classification. However,
unknown labels in partially annotated images exacerbate the positive-negative imbalance …

Multi-label classification (MLC) suffers from the inevitable label noise in training data due to
the difficulty in annotating various semantic labels in each image. To mitigate the influence …