Search Results for author:
Found 12 papers, 3 papers with code
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis
Current distillation techniques often dichotomize into two distinct aspects: i) ODE Trajectory Preservation; and ii) ODE Trajectory Reformulation.
UniFL: Improve Latent Diffusion Model via Unified Feedback Learning
Latent diffusion models (LDM) have revolutionized text-to-image generation, leading to the proliferation of various advanced models and diverse downstream applications.
ByteEdit: Boost, Comply and Accelerate Generative Image Editing
Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks.
ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
Especially, after learning a deep understanding of pure resolution priors, ResAdapter trained on the general dataset, generates resolution-free images with personalized diffusion models while preserving their original style domain.
Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs
To address this issue, we propose a novel framework based on contrastive learning, which can represent and balance the explicit and implicit relations for knowledge concept recommendation in MOOCs (CL-KCRec).
Sign Language Production with Latent Motion Transformer
The main goal of SLP is to create these videos using a sign gloss.
Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
Recent advances in text-to-image (T2I) diffusion models have enabled impressive image generation capabilities guided by text prompts.
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model
Our approach focuses on the transformation of sign gloss sequences into their corresponding sign pose sequences (G2P).
MvSR-NAT: Multi-view Subset Regularization for Non-Autoregressive Machine Translation
Conditional masked language models (CMLM) have shown impressive progress in non-autoregressive machine translation (NAT).
PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution
Since the superiority of Transformer in learning long-term dependency, the sign language Transformer model achieves remarkable progress in Sign Language Recognition (SLR) and Translation (SLT).
Multi-Scale Local-Temporal Similarity Fusion for Continuous Sign Language Recognition
Continuous sign language recognition (cSLR) is a public significant task that transcribes a sign language video into an ordered gloss sequence.
Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism
Concretely, we insert a left-to-right mask to the same decoder of CMTM, and then induce it to autoregressively review whether each generated word from CMTM is supposed to be replaced or kept.
