Search Results for author:
Found 13 papers, 7 papers with code
VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis
Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content.
Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive
Current L2I models either suffer from poor editability via text or weak alignment between the generated image and the input layout.
QPoser: Quantized Explicit Pose Prior Modeling for Controllable Pose Generation
Explicit pose prior models compress human poses into latent representations for using in pose-related downstream tasks.
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation
Within the context of autonomous driving, encountering unknown objects becomes inevitable during deployment in the open world.
Divide & Bind Your Attention for Improved Generative Semantic Nursing
To address the challenges posed by complex prompts or scenarios involving multiple entities and to achieve improved attribute binding, we propose Divide & Bind.
Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization
Using the proposed masked noise encoder to randomize style and content combinations in the training set, i. e., intra-source style augmentation (ISSA) effectively increases the diversity of training data and reduces spurious correlation.
Reliable machine learning potentials based on artificial neural network for graphene
Lattice parameter, coefficient of thermal expansion (CTE), Young's modulus and yield strength are estimated using machine learning accelerated MD simulations (MLMD), which are compared to experimental/first principle calculations from previous literatures.
Intra-Source Style Augmentation for Improved Domain Generalization
Using the proposed masked noise encoder to randomize style and content combinations in the training set, ISSA effectively increases the diversity of training data and reduces spurious correlation.
Category-Agnostic 6D Pose Estimation with Conditional Neural Processes
We present a novel meta-learning approach for 6D pose estimation on unknown objects.
Recommender Transformers with Behavior Pathways
We conclude this characteristic for sequential behaviors of each user as the Behavior Pathway.
MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation.
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction
Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction, which is one of the most typical machine learning tasks in personalized advertising and recommender systems.
Learning User Representations with Hypercuboids for Recommender Systems
Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests.
