3 accepted papers
Vision-language models like CLIP excel at zero-shot recognition but struggle with continual learning due to two critical issues: (1) severe distribution gap between pretraining captions and post-training class names, and (2) performance mismatch between vision-only and dual-encoder approaches--visio
Generalist Anomaly Detection (GAD) aims to train a unified model on an original domain that can detect anomalies in new target domains. Previous GAD methods primarily use only normal samples as references, overlooking the valuable information contained in anomalous samples that are often available i…
Most continual segmentation methods tackle the problem as a per-pixel classification task. However such a paradigm is very challenging and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones as objectness has strong transfer ability and forge…