conducts masked image modeling jointly with masked video modeling on video data. In particular, BEVT first performs masked image modeling on image data, and then. We introduce BEVT which decouples video representation learning into spatial representation learning and temporal dynamics learning. It is a straightforward but worth-studying extension given the recent success from BERT pretraining of image transformers. This paper studies the BERT pretraining of video transformers. Experimental results on Caltech 101, LFW, MNIST and RGB-D datasets demonstrate the effectiveness of our proposed methods. We further extend MIDSL and SMIDSL methods by kernel technique and propose kernelized multi-view intact discriminant space learning (KMIDSL) and kernelized semi-supervised multi-view intact discriminant space learning (KSMIDSL) methods. Aiming to utilize unlabeled samples to help mining more useful information for better learning latent intact discriminant space, we extend MIDSL method in semi-supervised scenario and propose semi-supervised multi-view intact discriminant space learning (SMIDSL) method. MIDSL can simultaneously minimize the within-class scatter and maximize the between-class scatter of the feature representations of different objects in the learned latent intact discriminant space. MIDSL learns a latent intact discriminant space by employing Fisher discrimination criterion to fully use class label information, which can well guide exploiting useful discriminant information, of labeled training samples. integrating complementary multi-view information of different views. In this paper, we propose a novel supervised latent subspace learning method called multi-view intact discriminant space learning (MIDSL) by efficiently. In multi-view learning, comprehensive utilization of multi-view information is helpful. ĭifferent views of one object usually represent different aspects of the object, and a single view is unlikely to comprehensively describe the object.
Autoprompt skip prompt archive#
We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. These prompts are usually manually created, and quite possibly sub-optimal another prompt such as “ Obama worked as a _ ” may result in more accurately predicting the correct profession. Recent work has presented intriguing results examining the knowledge contained in language models (LMs) by having the LM fill in the blanks of prompts such as “ Obama is a _ by profession”.
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