Search Results for author:
Found 7 papers, 3 papers with code
Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance
Classifier-Free Guidance (CFG) has been widely used in text-to-image diffusion models, where the CFG scale is introduced to control the strength of text guidance on the whole image space.
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
This paradigm extends the 2D image diffusion model to a volumetric version with a slightly increasing number of parameters and computation, offering a principled solution for generic cross-modality 3D medical image synthesis.
Align, Adapt and Inject: Sound-guided Unified Image Generation
Then, we propose the audio adapter to adapt audio representation into an audio token enriched with specific semantics, which can be injected into a frozen T2I model flexibly.
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Text-to-image generation has recently witnessed remarkable achievements.
Ranked #11 on
Text-to-Image Generation
on MS COCO
Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction
The HoP approach is straightforward: given the current timestamp t, we generate a pseudo Bird's-Eye View (BEV) feature of timestamp t-k from its adjacent frames and utilize this feature to predict the object set at timestamp t-k. Our approach is motivated by the observation that enforcing the detector to capture both the spatial location and temporal motion of objects occurring at historical timestamps can lead to more accurate BEV feature learning.
Ranked #3 on
3D Object Detection
on nuScenes Camera Only
Large-batch Optimization for Dense Visual Predictions
To address this challenge, we propose a simple yet effective algorithm, named Adaptive Gradient Variance Modulator (AGVM), which can train dense visual predictors with very large batch size, enabling several benefits more appealing than prior arts.
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach
Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning.
