Search Results for author:
Found 10 papers, 6 papers with code
RealFill: Reference-Driven Generation for Authentic Image Completion
Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene.
Emergent Correspondence from Image Diffusion
We propose a simple strategy to extract this implicit knowledge out of diffusion networks as image features, namely DIffusion FeaTures (DIFT), and use them to establish correspondences between real images.
Magic3D: High-Resolution Text-to-3D Content Creation
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results.
Ranked #2 on
Text to 3D
on T$^3$Bench
Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners
Existing approaches generally assume that the shot number at test time is known in advance.
Visual Prompt Tuning
The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning.
Ranked #2 on
Prompt Engineering
on ImageNet-21k
Few-Shot Classification with Feature Map Reconstruction Networks
In this paper we reformulate few-shot classification as a reconstruction problem in latent space.
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e. g., birds) based on a few images alone.
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification
In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed.
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities.
Hierarchical Deep Recurrent Architecture for Video Understanding
The proposed framework contains hierarchical deep architecture, including the frame-level sequence modeling part and the video-level classification part.
