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Found 4 papers, 2 papers with code
Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision.
Osprey: Pixel Understanding with Visual Instruction Tuning
In this paper, we propose Osprey, a mask-text instruction tuning approach, to extend MLLMs by incorporating fine-grained mask regions into language instruction, aiming at achieving pixel-wise visual understanding.
Text as Image: Learning Transferable Adapter for Multi-Label Classification
Pre-trained vision-language models have notably accelerated progress of open-world concept recognition.
Label-efficient Segmentation via Affinity Propagation
In this work, we formulate the affinity modeling as an affinity propagation process, and propose a local and a global pairwise affinity terms to generate accurate soft pseudo labels.
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