6). Furthermore, we validated the resolution improvement, quantifiability, and the generalization capability of ZS-DeconvNet (Supplementary Figs. 7–10), and compared it with the supervised DFCAN model8 (Supplementary Fig. 11) on synthetic and experimental data. These characterizations demonstrate ...
Zero-shot prompting is a technique that uses LLMs' generalization capabilities to attempt new tasks without prior specific training or examples. It uses LLMs' extensive pre-training on large and diverse datasets, enabling them to apply their broad knowledge to new tasks based solely on clear and...
One fascinating aspect of pre-trained vision-language models~(VLMs) learning under language supervision is their impressive zero-shot generalization capability. However, this ability is hindered by distribution shifts between the training and testing data. Previous test time adaptation~(TTA) methods for...
The excellent generalization capability of pre-trained Vision-Language Models (VLMs) makes fine-tuning VLMs for downstream zero-shot tasks a popular choice. Despite achieving promising performance in the professionality of base classes, most existing fine-tuned methods suffer from feature confusion of ...
Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks, and exhibit remarkable zero-shot generalization capability, while they are also vulnerable to imperceptible adversarial examples. Existing works typically employ adversarial training (fine-tuni...
robustness, and generalization.【10】IFAN: 以可解释性为重点的人类和NLP模型的交互框架 IFAN: An Exp...
We present TexDreamer , the first zero-shot multimodal high-fidelity3D human texture generation model. Utilizing an efficient texture adaptation finetuning strategy, we adapt large T2I model toa semantic UV structure while preserving its original generalization capability. Leveraging a novel feature ...
Training SAM with this dataset enables it to generate precise matte masks while maintaining its zero-shot capability. Second, we design the zero-shot matting model equipped with a hierarchical pixel decoder to enhance mask representation, along with a prompt-aware masked attention mechanism to ...
structural knowledge exhibited in prior category graphs can be effectively leveraged to boost the generalization capability of the learned projection function. Experiments on existing seen/unseen splits of three popular object detection datasets demonstrate that the proposed approach performs favorably against...
The flexible prompting support, ambiguity awareness, and vast training data endow the SAM with powerful generalization, enabling the ability to solve downstream segmentation problems using prompt engineering. Some following studies leverage the excellent zero-shot capability of SAM to solve other 2D ...