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Found 9 papers, 4 papers with code
UniForensics: Face Forgery Detection via General Facial Representation
On the basis of the representation learned in the first stage, the second stage involves fine-tuning on face forgery detection dataset to build a deepfake detector.
CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
Virtual try-on methods based on diffusion models achieve realistic try-on effects but often replicate the backbone network as a ReferenceNet or use additional image encoders to process condition inputs, leading to high training and inference costs.
Construction of a Syntactic Analysis Map for Yi Shui School through Text Mining and Natural Language Processing Research
Entity and relationship extraction is a crucial component in natural language processing tasks such as knowledge graph construction, question answering system design, and semantic analysis.
X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion
We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable.
Ranked #8 on
Instance Segmentation
on LVIS v1.0 val
Self-supervised Transformer for Deepfake Detection
After pre-training with our method, the model will then be partially fine-tuned for deepfake detection task.
Intelligent Online Selling Point Extraction for E-Commerce Recommendation
In addition, this kind of product description should be eye-catching to the readers.
Multi-attentional Deepfake Detection
Most of them model deepfake detection as a vanilla binary classification problem, i. e, first use a backbone network to extract a global feature and then feed it into a binary classifier (real/fake).
Improved Image Matting via Real-time User Clicks and Uncertainty Estimation
Image matting is a fundamental and challenging problem in computer vision and graphics.
An $O(N)$ Sorting Algorithm: Machine Learning Sort
We propose an $O(N\cdot M)$ sorting algorithm by Machine Learning method, which shows a huge potential sorting big data.
