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Found 14 papers, 8 papers with code
VividPose: Advancing Stable Video Diffusion for Realistic Human Image Animation
This enables accurate alignment of pose and shape in the generated videos, providing a robust framework capable of handling a wide range of body shapes and dynamic hand movements.
StyleMaster: Towards Flexible Stylized Image Generation with Diffusion Models
Stylized Text-to-Image Generation (STIG) aims to generate images based on text prompts and style reference images.
Towards Global Optimal Visual In-Context Learning Prompt Selection
Visual In-Context Learning (VICL) is a prevailing way to transfer visual foundation models to new tasks by leveraging contextual information contained in in-context examples to enhance learning and prediction of query sample.
A Generalization Theory of Cross-Modality Distillation with Contrastive Learning
For instance, one typically aims to minimize the L2 distance or contrastive loss between the learned features of pairs of samples in the source (e. g. image) and the target (e. g. sketch) modalities.
DiffFAE: Advancing High-fidelity One-shot Facial Appearance Editing with Space-sensitive Customization and Semantic Preservation
Facial Appearance Editing (FAE) aims to modify physical attributes, such as pose, expression and lighting, of human facial images while preserving attributes like identity and background, showing great importance in photograph.
Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images
For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive.
Exploring Efficient Few-shot Adaptation for Vision Transformers
Essentially, despite ViTs have been shown to enjoy comparable or even better performance on other vision tasks, it is still very nontrivial to efficiently finetune the ViTs in real-world FSL scenarios.
Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learning
Since only part of the most confident positive samples are available and evidence is not enough to categorize the rest samples, many of these unlabeled data may also be the positive samples.
PatchMix Augmentation to Identify Causal Features in Few-shot Learning
We theoretically show that such an augmentation mechanism, different from existing ones, is able to identify the causal features.
The Image Local Autoregressive Transformer
To address these limitations, we propose a novel model -- image Local Autoregressive Transformer (iLAT), to better facilitate the locally guided image synthesis.
Learning Dynamic Alignment via Meta-filter for Few-shot Learning
Our insight is that these methods would lead to poor adaptation with redundant matching, and leveraging channel-wise adjustment is the key to well adapting the learned knowledge to new classes.
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization
Temporal action localization is an important yet challenging task in video understanding.
Instance Credibility Inference for Few-Shot Learning
To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree.
Learning to score the figure skating sports videos
This paper targets at learning to score the figure skating sports videos.
