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Found 5 papers, 5 papers with code
CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning
Large pre-trained VLMs like CLIP have demonstrated superior zero-shot recognition ability, and a number of recent studies leverage this ability to mitigate catastrophic forgetting in CL, but they focus on closed-set CL in a single domain dataset.
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
In this work, we propose a novel training mechanism termed SegPrompt that uses category information to improve the model's class-agnostic segmentation ability for both known and unknown categories.
Learning Conditional Attributes for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations.
Ranked #1 on
Compositional Zero-Shot Learning
on MIT-States
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-Language
Compositional generalization is critical to simulate the compositional capability of humans, and has received much attention in the vision-and-language (V&L) community.
Maintaining Reasoning Consistency in Compositional Visual Question Answering
Existing VQA models can answer a compositional question well, but cannot work well in terms of reasoning consistency in answering the compositional question and its sub-questions.
