Search Results for author:
Found 12 papers, 6 papers with code
Retrieval-Augmented Score Distillation for Text-to-3D Generation
We leverage the retrieved asset to incorporate its geometric prior in the variational objective and adapt the diffusion model's 2D prior toward view consistency, achieving drastic improvements in both geometry and fidelity of generated scenes.
NVS-Adapter: Plug-and-Play Novel View Synthesis from a Single Image
Transfer learning of large-scale Text-to-Image (T2I) models has recently shown impressive potential for Novel View Synthesis (NVS) of diverse objects from a single image.
Variational Distribution Learning for Unsupervised Text-to-Image Generation
We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training.
Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers
However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregressive process.
Ranked #24 on
Video Generation
on UCF-101
Generalizable Implicit Neural Representations via Instance Pattern Composers
Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances.
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer
After code stacks in the sequence are randomly masked, Contextual RQ-Transformer is trained to infill the masked code stacks based on the unmasked contexts of the image.
Ranked #1 on
Text-to-Image Generation
on Conceptual Captions
Autoregressive Image Generation using Residual Quantization
However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off.
Ranked #2 on
Text-to-Image Generation
on Conceptual Captions
Contrastive Regularization for Semi-Supervised Learning
Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance.
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering
The results imply that cross-modal attention in VQA is important to improve not only VQA accuracy, but also the robustness to various anomalies.
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks
Soft labeling becomes a common output regularization for generalization and model compression of deep neural networks.
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
TGNet learns an autoregressive model, conditioned on temporal contexts of forecasting targets from temporal-guided embedding.
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis
In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods.
