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Found 14 papers, 9 papers with code
The Devil is in the Few Shots: Iterative Visual Knowledge Completion for Few-shot Learning
Few-shot learning aims to further enhance the transfer capability of CLIP by giving few images in each class, aka 'few shots'.
Conditional Prototype Rectification Prompt Learning
Recent advancements in efficient transfer learning (ETL) have shown remarkable success in fine-tuning VLMs within the scenario of limited data, introducing only a few parameters to harness task-specific insights from VLMs.
Segment Anything Model Meets Image Harmonization
Image harmonization is a crucial technique in image composition that aims to seamlessly match the background by adjusting the foreground of composite images.
Boosting Audio-visual Zero-shot Learning with Large Language Models
Audio-visual zero-shot learning aims to recognize unseen categories based on paired audio-visual sequences.
Ranked #1 on
GZSL Video Classification
on ActivityNet-GZSL (cls)
DiffUTE: Universal Text Editing Diffusion Model
Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information.
Mobile User Interface Element Detection Via Adaptively Prompt Tuning
Recent object detection approaches rely on pretrained vision-language models for image-text alignment.
DiffusionInst: Diffusion Model for Instance Segmentation
Extensive experimental results on COCO and LVIS show that DiffusionInst achieves competitive performance compared to existing instance segmentation models with various backbones, such as ResNet and Swin Transformers.
Ranked #8 on
Instance Segmentation
on LVIS v1.0 val
Hierarchical Dynamic Image Harmonization
The MGD effectively applies distinct convolution to the foreground and background, learning the representations of foreground and background regions as well as their correlations to the global harmonization, facilitating local visual consistency for the images much more efficiently.
Ranked #2 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains.
Shaping Visual Representations with Attributes for Few-Shot Recognition
Under the guidance of attribute modality, our method can learn enhanced semantic-aware representation for classification.
Sparse Spatial Transformers for Few-Shot Learning
Finally, we propose using an image patch-matching module to calculate the distance between dense local representations, thus determining which category the query image belongs to in the support set.
Ranked #16 on
Few-Shot Image Classification
on FC100 5-way (1-shot)
Multi-level Metric Learning for Few-shot Image Recognition
Moreover, a Multi-level Metric Learning (MML) method is proposed, which not only calculates the pixel-level similarity but also considers the similarity of part-level features and global-level features.
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification
Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data.
Multi-scale Adaptive Task Attention Network for Few-Shot Learning
Then, an adaptive task attention module is proposed to select the most important LRs among the entire task.
