Search Results for author:
Found 14 papers, 5 papers with code
DynamicFace: High-Quality and Consistent Video Face Swapping using Composable 3D Facial Priors
We propose a novel method DynamicFace that leverages the power of diffusion model and plug-and-play temporal layers for video face swapping.
Single Trajectory Distillation for Accelerating Image and Video Style Transfer
A promising solution to speed up the process is to obtain few-step consistency models through trajectory distillation.
P4Q: Learning to Prompt for Quantization in Visual-language Models
We propose a method that balances fine-tuning and quantization named ``Prompt for Quantization'' (P4Q), in which we design a lightweight architecture to leverage contrastive loss supervision to enhance the recognition performance of a PTQ model.
Pushing the Boundaries of Text to Motion with Arbitrary Text: A New Task
Text to Motion aims to generate human motions from texts.
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification
The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the base dataset.
Self-Enhancement Improves Text-Image Retrieval in Foundation Visual-Language Models
The emergence of cross-modal foundation models has introduced numerous approaches grounded in text-image retrieval.
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Text consists of a category name and a fixed number of learnable parameters which are selected from our designed attribute word bank and serve as attributes.
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
However, with only a few training images, there exist two crucial problems: (1) the visual feature distributions are easily distracted by class-irrelevant information in images, and (2) the alignment between the visual and language feature distributions is difficult.
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning
Based on this observation, we propose a simple strategy, i. e., increasing the number of training shots, to mitigate the loss of intrinsic dimension caused by robustness-promoting regularization.
Anti-Retroactive Interference for Lifelong Learning
Second, according to the similarity between incremental knowledge and base knowledge, we design an adaptive fusion of incremental knowledge, which helps the model allocate capacity to the knowledge of different difficulties.
Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation
Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention.
Associative Adversarial Learning Based on Selective Attack
We show that our method improves the recognition accuracy of adversarial training on ImageNet by 8. 32% compared with the baseline.
Cogradient Descent for Dependable Learning
Conventional gradient descent methods compute the gradients for multiple variables through the partial derivative.
IDARTS: Interactive Differentiable Architecture Search
Differentiable Architecture Search (DARTS) improves the efficiency of architecture search by learning the architecture and network parameters end-to-end.
