Single CLIP
Single CLIP, a powerful vision-language model, is being extensively studied to improve its performance and address its limitations in various applications. Current research focuses on mitigating issues like object hallucinations, enhancing its capabilities for specialized domains (e.g., agriculture), and developing robust defenses against adversarial attacks and biases. This work is significant because it explores ways to leverage CLIP's impressive zero-shot capabilities while simultaneously improving its accuracy, reliability, and fairness across diverse downstream tasks, impacting fields ranging from image generation to anomaly detection.
Papers
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
Yunkang Cao, Jiangning Zhang, Luca Frittoli, Yuqi Cheng, Weiming Shen, Giacomo Boracchi
CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning
Emanuele Frascaroli, Aniello Panariello, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara