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Found 14 papers, 4 papers with code
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models
Large-scale vision-language models (VLMs) have shown a strong zero-shot generalization capability on unseen-domain data.
Language-Guided Transformer for Federated Multi-Label Classification
Nevertheless, it is still challenging for FL to deal with user heterogeneity in their local data distribution in the real-world FL scenario, and this issue becomes even more severe in multi-label image classification.
Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers
The former refrains the model from producing images associated with the target concept for any paraphrased or learned prompts, while the latter preserves its ability in generating images with non-target concepts.
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation
To leverage robust representations from large-scale models while enabling efficient model personalization for heterogeneous clients, we propose a novel personalized FL framework of client-specific Prompt Generation (pFedPG), which learns to deploy a personalized prompt generator at the server for producing client-specific visual prompts that efficiently adapts frozen backbones to local data distributions.
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators
In this paper, we present a learning framework of Tendency-and-Assignment Explainer (TAX), designed to offer interpretability at the annotator and assignment levels.
Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and Beyond
In particular, we present a cross-scale patch-level correlation learning in SS-PRL, which allows the model to aggregate and associate information learned across patch scales.
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest.
Adversarial Teacher-Student Representation Learning for Domain Generalization
Domain generalization (DG) aims to transfer the learning task from a single or multiple source domains to unseen target domains.
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation
Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest.
LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity
To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper.
Dual-MTGAN: Stochastic and Deterministic Motion Transfer for Image-to-Video Synthesis
Generating videos with content and motion variations is a challenging task in computer vision.
LayoutTransformer: Relation-Aware Scene Layout Generation
In the areas of machine learning and computer vision, text-to-image synthesis aims at producing image outputs given the input text.
Semantics-Guided Representation Learning with Applications to Visual Synthesis
Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition.
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras.
Ranked #19 on
Unsupervised Domain Adaptation
on Duke to Market
