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Found 5 papers, 2 papers with code
Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations
We introduce CHARM, the first benchmark for comprehensively and in-depth evaluating the commonsense reasoning ability of large language models (LLMs) in Chinese, which covers both globally known and Chinese-specific commonsense.
Attentive Mask CLIP
To address this issue, we propose an attentive token removal approach for CLIP training, which retains tokens with a high semantic correlation to the text description.
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network
With the utilization of such a framework, the influence of non-robust features could be mitigated to strengthen the adversarial robustness.
Trusted Multi-Scale Classification Framework for Whole Slide Image
Leveraging the Vision Transformer as the backbone for multi branches, our framework can jointly classification modeling, estimating the uncertainty of each magnification of a microscope and integrate the evidence from different magnification.
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning
In particular, most existing unsupervised and domain adaptation ReID methods utilize only the public datasets in their experiments, with labels removed.
