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Found 2 papers, 1 papers with code
MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding
To address this issue, we propose \modelname, a simple yet effective multi-layer feature fuser that efficiently integrates deep and shallow features from Vision Transformers (ViTs).
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Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation
Second, to resist overfitting issues caused by few training samples, a hyper-class embedding is learned by clustering all category embeddings for initialization and aligned with category embedding of the new class for enhancement, where learned knowledge assists to learn new knowledge, thus alleviating performance dependence on training data scale.
