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Found 8 papers, 3 papers with code
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery
In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes.
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
Central to our approach is modeling a unique prompt tailored for detecting unknown class samples, and to train this, we employ a readily accessible stable diffusion model, elegantly generating proxy images for the open class.
C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing
To this end, we introduce C-SAW, a method that complements CLIP with a self-supervised loss in the visual space and a novel prompt learning technique that emphasizes both visual domain and content-specific features.
HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues
Even though joint training of audio-visual modalities improves classification performance in a low-data regime, it has yet to be thoroughly investigated in the RS domain.
GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised Learning
GOPro is trained end-to-end on all three loss objectives, combining the strengths of CLIP and SSL in a principled manner.
AD-CLIP: Adapting Domains in Prompt Space Using CLIP
We leverage the frozen vision backbone of CLIP to extract both image style (domain) and content information, which we apply to learn prompt tokens.
APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP
APPLeNet emphasizes the importance of multi-scale feature learning in RS scene classification and disentangles visual style and content primitives for domain generalization tasks.
StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization
Our method focuses on a domain-agnostic prompt learning strategy, aiming to disentangle the visual style and content information embedded in CLIP's pre-trained vision encoder, enabling effortless adaptation to novel domains during inference.
