Search Results for author:
Found 6 papers, 4 papers with code
FEEL: A Framework for Evaluating Emotional Support Capability with Large Language Models
The model meticulously considers var-ious evaluative aspects of ESC to apply a more comprehensive and accurate evaluation method for ESC.
APLe: Token-Wise Adaptive for Multi-Modal Prompt Learning
Pre-trained Vision-Language (V-L) models set the benchmark for generalization to downstream tasks among the noteworthy contenders.
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.
C2ST: Cross-Modal Contextualized Sequence Transduction for Continuous Sign Language Recognition
In this paper, we propose a Cross-modal Contextualized Sequence Transduction (C2ST) for CSLR, which effectively incorporates the knowledge of gloss sequence into the process of video representation learning and sequence transduction.
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.
Efficient Graph Deep Learning in TensorFlow with tf_geometric
We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1. x and 2. x.
