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Found 17 papers, 5 papers with code
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion
To our best knowledge, this is the first work to offer an efficient second-order approximation in solving inverse problems using latent diffusion and editing real-world images with corruptions.
Distributionally Robust Post-hoc Classifiers under Prior Shifts
We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors.
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations.
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance.
Solving Inverse Problems with NerfGANs
We introduce a novel framework for solving inverse problems using NeRF-style generative models.
Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval
We present Retrieve in Style (RIS), an unsupervised framework for facial feature transfer and retrieval on real images.
Improved Detection of Face Presentation Attacks Using Image Decomposition
Presentation attack detection (PAD) is a critical component in secure face authentication.
Few-Shot Adaptation of Generative Adversarial Networks
We validate our method in a challenging few-shot setting of 5-100 images in the target domain.
Learning Facial Action Units From Web Images With Scalable Weakly Supervised Clustering
We present a scalable weakly supervised clustering approach to learn facial action units (AUs) from large, freely available web images.
Modeling Spatial and Temporal Cues for Multi-label Facial Action Unit Detection
To model temporal dependencies, Long Short-Term Memory (LSTMs) are stacked on top of these representations, regardless of the lengths of input videos.
Deep Region and Multi-Label Learning for Facial Action Unit Detection
Region learning (RL) and multi-label learning (ML) have recently attracted increasing attentions in the field of facial Action Unit (AU) detection.
Ranked #6 on
Facial Action Unit Detection
on DISFA
An Empirical Study of Dimensional Reduction Techniques for Facial Action Units Detection
We compared linear (PCA and KPCA), manifold (LPP and LLE), supervised (LDA and KDA) and hybrid approaches (LSDA) to DR with respect to AU detection.
Confidence Preserving Machine for Facial Action Unit Detection
Varied sources of error contribute to the challenge of facial action unit detection.
Unsupervised Synchrony Discovery in Human Interaction
We evaluate the effectiveness of our approach in multiple databases, including human actions using the CMU Mocap dataset, spontaneous facial behaviors using group-formation task dataset and parent-infant interaction dataset.
Joint Patch and Multi-Label Learning for Facial Action Unit Detection
The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS).
Video Co-Summarization: Video Summarization by Visual Co-Occurrence
We present video co-summarization, a novel perspective to video summarization that exploits visual co-occurrence across multiple videos.
Selective Transfer Machine for Personalized Facial Action Unit Detection
To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2].
