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Found 8 papers, 0 papers with code
Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice.
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition
To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts.
Disentangled Representation Learning with Transmitted Information Bottleneck
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models.
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement
Dominant Person Search methods aim to localize and recognize query persons in a unified network, which jointly optimizes two sub-tasks, \ie, pedestrian detection and Re-IDentification (ReID).
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation
Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls.
Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History
Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module.
Disentangled Generation with Information Bottleneck for Few-Shot Learning
To these challenges, we propose a novel Information Bottleneck (IB) based Disentangled Generation Framework for FSL, termed as DisGenIB, that can simultaneously guarantee the discrimination and diversity of generated samples.
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search
On the other hand, there are numerous unpaired persons in real-world scene images.
