@InProceedings{Zheng_2024_Large, title={Large Language Models are Good Prompt Learners for Low-Shot Image Classification}, author={Zheng, Zhaoheng and Wei, Jingmin and Hu, Xuefeng and Zhu, Haidong and Nevatia, Ram}, booktitle = {CVPR}, year = {2024}, } ...
A 2022 paper by Deng et al. [43] deals with “intra-class and inter-class” data imbalance within the task of one-shot image classification for road objects, proposing a novel GAN model named PcGAN. Compared to traditional GAN models, PcGAN places more focus on learning a robust embedding ...
These annotations are especially limited for semantic segmentation, or pixel-wise classification, of remote sensing imagery because it is labor intensive to generate image annotations. Low-shot learning algorithms can make effective inferences despite smaller amounts of annotated data. In this paper, we...
The size of current plankton image datasets renders manual classification virtually infeasible. The training of models for machine classification is complicated by the fact that a large number of classes consist of only a few examples. We employ the recently introduced weight imprinting technique in ...
In the new training only the new Classify() head has it's gradients turned on, so the entire backbone is frozen. You can see in my screenshot from today's classifier training it says 1192362 parameters, 2570 gradients, whereas in the March 2021 results it shows 1194954 parameters, 1194954...
Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario, since object detection has an additional challenging localization task. Low-Shot Object ...
Deep neural networks have demonstrated advanced abilities on various visual classification tasks, which heavily rely on the large-scale training samples with annotated ground-truth. However, it is unrealistic always to require such annotation in real-world applications. Recently, Few-Shot learning (FS)...
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