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Found 8 papers, 7 papers with code
Erasing Self-Supervised Learning Backdoor by Cluster Activation Masking
After obtaining the threat model trained on the poisoned dataset, our method can precisely detect poisonous samples based on the assumption that masking the backdoor trigger can effectively change the activation of a downstream clustering model.
A Survey on Interpretable Cross-modal Reasoning
In recent years, cross-modal reasoning (CMR), the process of understanding and reasoning across different modalities, has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics.
Open-World Social Event Classification
To tackle these challenges, we propose an Open-World Social Event Classifier (OWSEC) model in this paper.
Variational Causal Inference Network for Explanatory Visual Question Answering
To address these issues, we propose a Variational Causal Inference Network (VCIN) that establishes the causal correlation between predicted answers and explanations, and captures cross-modal relationships to generate rational explanations.
Ranked #1 on
Explanatory Visual Question Answering
on GQA-REX
Explanation Generation
Explanatory Visual Question Answering
+2
MMT: Image-guided Story Ending Generation with Multimodal Memory Transformer
Finally, a multimodal transformer decoder constructs attention among multimodal features to learn the story dependency and generates informative, reasonable, and coherent story endings.
Ranked #1 on
Image-guided Story Ending Generation
on LSMDC-E
Integrating multi-label contrastive learning with dual adversarial graph neural networks for cross-modal retrieval
To date, most of the existing techniques mainly convert multimodal data into a common representation space where similarities in semantics between samples can be easily measured across multiple modalities.
Adaptive label-aware graph convolutional networks for cross-modal retrieval
Firstly, we construct an instance representation learning branch to transform instances of different modalities into a common representation space.
Dual adversarial graph neural networks for multi-label cross-modal retrieval
To date, most existing methods transform multimodal data into a common representation space where semantic similarities between items can be directly measured across different modalities.
