Regress in a sentence3/28/2023 ![]() Finally, a novel attention based location prediction network is designed to regress the temporal coordinates of sentence from the previous attentions. The former reflects the global video structure, while the latter highlights the sentence details for temporal localization. Then, a multi-modal co-attention mechanism is presented to generate both video and sentence attentions. Specifically, to preserve the context information, ABLR first encodes both video and sentence via Bi-directional LSTM networks. ![]() To address these issues, we propose a novel Attention Based Location Regression (ABLR) approach to localize sentence descriptions in videos in an efficient end-to-end manner. For solving this problem, we face three critical challenges: (1) preserving the intrinsic temporal structure and global context of video to locate accurate positions over the entire video sequence (2) fully exploring the sentence semantics to give clear guidance for localization (3) ensuring the efficiency of the localization method to adapt to long videos. This motivates us to explore an overlooked problem in the research community - temporal sentence localization in video, which aims to automatically determine the start and end points of a given sentence within a paired video. Therefore, it has been increasingly crucial to associate specific video segments with the corresponding informative text descriptions, for a deeper understanding of video content. We have witnessed the tremendous growth of videos over the Internet, where most of these videos are typically paired with abundant sentence descriptions, such as video titles, captions and comments. create ( 1, "Logistic regression models are neat" ) )) StructType schema = new StructType ( new StructField RegexTokenizer regexTokenizer = new RegexTokenizer (). create ( 0, "I wish Java could use case classes" ), RowFactory. create ( 0, "Hi I heard about Spark" ), RowFactory. Import import import .java.JavaRDD import .java.JavaSparkContext import .feature.HashingTF import .feature.IDF import .feature.IDFModel import .feature.Tokenizer import .linalg.Vector import .DataFrame import .Row import .RowFactory import .SQLContext import .types.DataTypes import .types.Metadata import .types.StructField import .types.StructType JavaRDD jrdd = jsc. Our feature vectors could then be passed to a learning algorithm. We use IDF to rescale the feature vectors this generally improves performance when using text as features. For each sentence (bag of words), we use HashingTF to hash the sentence into a feature vector. We split each sentence into words using Tokenizer. In the following code segment, we start with a set of sentences. Please refer to the MLlib user guide on TF-IDF for more details on Term Frequency and Inverse Document Frequency. Intuitively, it down-weights columns which appear frequently in a corpus. The IDFModel takes feature vectors (generally created from HashingTF) and scales each column. IDF: IDF is an Estimator which fits on a dataset and produces an IDFModel. The algorithm combines Term Frequency (TF) counts with the hashing trick for dimensionality reduction. In text processing, a “set of terms” might be a bag of words. TF: HashingTF is a Transformer which takes sets of terms and converts those sets into fixed-length feature vectors. In Spark ML, TF-IDF is separate into two parts: TF (+hashing) and IDF. Term Frequency-Inverse Document Frequency (TF-IDF) is a common text pre-processing step. Selection: Selecting a subset from a larger set of featuresįeature Extractors TF-IDF (HashingTF and IDF).Transformation: Scaling, converting, or modifying features.Extraction: Extracting features from “raw” data.This section covers algorithms for working with features, roughly divided into these groups: Extracting, transforming and selecting features - spark.ml
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