Search Results for author:
Found 4 papers, 0 papers with code
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
In this paper, we aim to develop open-vocabulary object detection (OVD) technique in aerial images that scales up object vocabulary size beyond training data.
On the Evaluation and Refinement of Vision-Language Instruction Tuning Datasets
Instead of evaluating the models directly, in this paper, we try to evaluate the Vision-Language Instruction-Tuning (VLIT) datasets.
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios
In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown classes (i. e., not seen during training), they always predict them as one of the training classes.
Rethinking Visual Prompt Learning as Masked Visual Token Modeling
To explore prompt learning on the generative pre-trained visual model, as well as keeping the task consistency, we propose Visual Prompt learning as masked visual Token Modeling (VPTM) to transform the downstream visual classification into the pre-trained masked visual token prediction.
