Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …

Spurred by advancements in scale, large language models (LLMs) have demonstrated the
ability to perform a variety of natural language processing (NLP) tasks zero-shot--ie, without …

Contrastive language-image pretraining has shown great success in learning visual-textual
joint representation from web-scale data, demonstrating remarkable “zero-shot” …

Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training
(CLIP) have shown inspirational performance on 2D visual recognition, which learns to …

Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP
model. Since training on a similar scale for videos is infeasible, recent approaches focus on …

Recent developments in multimodal training methodologies, including CLIP and ALIGN,
obviate the necessity for individual data labeling. These approaches utilize pairs of data and …

Adopting contrastive image-text pretrained models like CLIP towards video classification has
gained attention due to its cost-effectiveness and competitive performance. However, recent …

From the beginning of zero-shot learning research, visual attributes have been shown to
play an important role. In order to better transfer attribute-based knowledge from known to …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …