[05] Gaze-directed Vision GNN for Mitigating Shortcut Learning in Medical Image 1948 -- 2:05 App [33] Intelligent Preoperative Diagnosis and Surgical Planning 612 -- 1:57 App [23] Self-supervised neural network-based endoscopic monocular 3D reconstruction 530 -- 2:02 App [11] Exploiting Geo...
Spatial-frequency prompt learningTest-time trainingGeneralizationDiffusion modelThe significance of face forgery detection has grown substantially due to the emergence of facial manipulation technologies. Recent methods have turned to face detection forgery in the spatial-frequency domain, resulting in improved...
On the one hand, the frequency representation can disentangle image degradation and content components, which makes learning the degradation information more effective. On the other hand, the frequency domain naturally encodes the globally distributed degradation-specific information. We exploit FSP to ...
We propose an effective Transformer-prompted network, TPNet, which utilizes prompt learning with a Transformer to guide the CNN in addressing AVS tasks. Specifically, during feature encoding, we incorporate a frequency-based prompt-supplement module to fine-tune and enhance the encoded features through...
4.2 Results on domain-incremental learning4.3 Results on task-agnostic learning下面这个图展示了prompt 与 task 对应的id被选中的频率,可见,对于task之间差异不大的情况,相似的prompt会被频繁调用,而在task差异较大的情况,相似的prompt被调用得比较少4.4 Abalation Study:Effect of prompt related components for ...
Chen W, Huang Z, Tsai CC et al (2022) Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 17653–17662 Chen X, Pan J,...
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates. - prompt-in-context-learning/PromptEngineering.md at main · EgoAlpha/prompt-in-context-le
Learning Outcomes: At the completion of the PROMPT Certification process, clinicians will have: Analyzed all client domains to evaluate the strengths and weaknesses the client brings to thecommunication interaction. The assessment areas include: physical-sensory, cognitive-linguistic,social-emotional and...
Conditional Prompt Learning for Vision-Language Models Kaiyang Zhou Jingkang Yang Chen Change Loy Ziwei Liu S-Lab, Nanyang Technological University, Singapore {kaiyang.zhou, jingkang001, ccloy, ziwei.liu}@ntu.edu.sg Abstract With the rise of powerful pre-trained vision-language...
Pros: Diffbot uses advanced machine learning technology to automatically turn web pages into structured data. It’s powerful for large-scale web data extraction projects and offers an extensive range of APIs for different types of data. Cons: Its advanced technology and features come at a higher ...