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Found 5 papers, 2 papers with code
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models
This work studies the adversarial robustness of VLMs from the novel perspective of the text prompt instead of the extensively studied model weights (frozen in this work).
Rethinking the backbone architecture for tiny object detection
Tiny object detection has become an active area of research because images with tiny targets are common in several important real-world scenarios.
Query Semantic Reconstruction for Background in Few-Shot Segmentation
To successfully associate prototypes with class labels and extract a background prototype that is capable of predicting a mask for the background regions of the image, the machinery for extracting and using foreground prototypes is induced to become more discriminative between different classes.
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation
To overcome this issue, we propose CobNet which utilises information about the background that is extracted from the query images without annotations of those images.
Registration based Few-Shot Anomaly Detection
Inspired by how humans detect anomalies, i. e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model.
Ranked #72 on
Anomaly Detection
on MVTec AD
