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Found 5 papers, 0 papers with code
Large Language Models are Zero Shot Hypothesis Proposers
We subsequently evaluate the hypothesis generation capabilities of various top-tier instructed models in zero-shot, few-shot, and fine-tuning settings, including both closed and open-source LLMs.
Non-Local ConvLSTM for Video Compression Artifact Reduction
Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos.
Versatile Multiple Choice Learning and Its Application to Vision Computing
Most existing ensemble methods aim to train the underlying embedded models independently and simply aggregate their final outputs via averaging or weighted voting.
Learning Competitive and Discriminative Reconstructions for Anomaly Detection
Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier.
Global Semantic Consistency for Zero-Shot Learning
In this paper, we propose an end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes, to support effective zero-shot learning.
