pascal-voc
Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection
Out-of-distribution (OOD) detection is essential for reliably deploying machine learning models in the wild. Yet, most methods treat large pre-trained models as monolithic encoders and rely solely on their final-layer representations for detection.
SeT AR: Out-of-Distribution Detection with Selective Low-Rank Approximation
SeT AR enhances OOD detection via post-hoc modification of the model's
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation (Supplementary material) Anonymous Author(s) Affiliation Address email
In the supplementary material, we first introduce technical details of the "frozen CLIP" approaches in Sec. 1. Then the dataset settings are shown in Sec. 2. Figure 1 presents an overview of the "frozen CLIP" approach. It's worth noting that all sub-images are resized to Figure 2: Comparison among three merge operations. Pascal-VOC, COCO-Stuff and ADE20K, to evaluate the performance of MAFT. Pascal-VOC: There are 10582 images for training and 1,449 images for testing. ADE20K: ADE20K contains 25k images for training and 2k images for validation. Pascal-Context is an extensive dataset of Pascal-VOC 2010.
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation Siyu Jiao 1,2,3, Y unchao Wei 1,2,3, Y aowei Wang
Recently, pre-trained vision-language models have been increasingly used to tackle the challenging zero-shot segmentation task.
SeT AR: Out-of-Distribution Detection with Selective Low-Rank Approximation
SeT AR enhances OOD detection via post-hoc modification of the model's
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation Siyu Jiao 1,2,3, Y unchao Wei 1,2,3, Y aowei Wang
Recently, pre-trained vision-language models have been increasingly used to tackle the challenging zero-shot segmentation task.
