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Found 7 papers, 3 papers with code
Shackled Dancing: A Bit-Locked Diffusion Algorithm for Lossless and Controllable Image Steganography
Data steganography aims to conceal information within visual content, yet existing spatial- and frequency-domain approaches suffer from trade-offs between security, capacity, and perceptual quality.
MLLM-Enhanced Face Forgery Detection: A Vision-Language Fusion Solution
In this paper, we propose VLF-FFD, a novel Vision-Language Fusion solution for MLLM-enhanced Face Forgery Detection.
WMamba: Wavelet-based Mamba for Face Forgery Detection
Extensive experimental results show that WMamba achieves state-of-the-art (SOTA) performance, highlighting its effectiveness and superiority in face forgery detection.
S2TD-Face: Reconstruct a Detailed 3D Face with Controllable Texture from a Single Sketch
Furthermore, S2TD-Face introduces a texture control module utilizing text prompts to select the most suitable textures from a library and seamlessly integrate them into the geometry, resulting in a 3D detailed face with controllable texture.
3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation
In this paper, we fully utilize the facial part segmentation geometry by introducing Part Re-projection Distance Loss (PRDL).
Ranked #3 on
3D Face Reconstruction
on REALY (side-view)
Safe Self-Refinement for Transformer-based Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain.
Ranked #4 on
Domain Adaptation
on Office-31
Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models.
