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Concept-Guided Prompt Learning for Generalization in Vision-Language Models
However, for generalization tasks, the current fine-tuning methods for CLIP, such as CoOp and CoCoOp, demonstrate relatively low performance on some fine-grained datasets.
BDC-Adapter: Brownian Distance Covariance for Better Vision-Language Reasoning
Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP and ALIGN, have introduced a new paradigm for learning transferable visual representations.
Cross-Modal Concept Learning and Inference for Vision-Language Models
Based on these visual concepts, we construct a discriminative representation of images and learn a concept inference network to perform downstream image classification tasks, such as few-shot learning and domain generalization.
Cross-Inferential Networks for Source-free Unsupervised Domain Adaptation
Our main idea is that, when we adapt the network model to predict the sample labels from encoded features, we use these prediction results to construct new training samples with derived labels to learn a new examiner network that performs a different but compatible task in the target domain.
Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation
This strong correlation suggests that we can use this error as feedback to guide the correction process.
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
We observe that the performance of this feed-forward Hebbian learning for fully test-time adaptation can be significantly improved by incorporating a feedback neuro-modulation layer.
