Below is an abstract from a computer science research paper. Given this abstract, your task is to extract all unique relationships between entities of the following types:

- "USED-FOR": For instance, "Our method models user proficiency" -> ["method", "user proficiency", "USED-FOR"].
- "EVALUATE-FOR": For instance, "We evaluate our method using F1-measure" -> ["F1-measure", "method", "EVALUATE-FOR"].
- "FEATURE-OF": For instance, "prior knowledge of the model" -> ["prior knowledge", "model", "FEATURE-OF"].
- "HYPONYM-OF": For instance, "TUIT is a software library" -> ["TUIT", "software library", "HYPONYM-OF"].
- "PART-OF": For instance, "We incorporate NLU module into the system." -> ["NLU module", "system", "PART-OF"].
- "COMPARE": For instance, "Unlike the quantitative prior, the qualitative prior is often ignored" -> ["quantitative prior", "qualitative prior", "COMPARE"].
- "CONJUNCTION": For instance: "obtained from human expert or knowledge base" -> ["human expert", "knowledge base", "CONJUNCTION"].

Format your output as a json array. Each entry in the array should express a single relation, formatted as ["<Entity_A>", "<Entity_B>", "<RELATION_A_B>"]. An example output might look like:

[["neural networks", "classification", "USED-FOR"], ["neuron", "neural network", "PART-OF"]].

If no relations are found, please return an empty array [].

Abstract:

In this paper we target at generating generic action proposals in unconstrained videos. Each action proposal corresponds to a temporal series of spatial bounding boxes, i.e., a spatio-temporal video tube, which has a good potential to locate one human action. Assuming each action is performed by a human with meaningful motion, both appearance and motion cues are utilized to measure the ac-tionness of the video tubes. After picking those spatiotem-poral paths of high actionness scores, our action proposal generation is formulated as a maximum set coverage problem, where greedy search is performed to select a set of action proposals that can maximize the overall actionness score. Compared with existing action proposal approaches, our action proposals do not rely on video segmentation and can be generated in nearly real-time. Experimental results on two challenging datasets, MSRII and UCF 101, validate the superior performance of our action proposals as well as competitive results on action detection and search.
[["unconstrained videos", "generic action proposals", "USED-FOR"], ["spatio-temporal video tube", "temporal series of spatial bounding boxes", "HYPONYM-OF"], ["action detection and search", "action proposals", "EVALUATE-FOR"], ["spatio-temporal video tube", "human action", "USED-FOR"], ["appearance and motion cues", "ac-tionness", "USED-FOR"], ["actionness score", "action proposals", "EVALUATE-FOR"], ["datasets", "action proposals", "EVALUATE-FOR"], ["MSRII", "datasets", "HYPONYM-OF"], ["MSRII", "UCF 101", "CONJUNCTION"], ["UCF 101", "datasets", "HYPONYM-OF"], ["greedy search", "action proposals", "USED-FOR"], ["ac-tionness", "video tubes", "EVALUATE-FOR"], ["maximum set coverage problem", "action proposal generation", "USED-FOR"], ["action proposal approaches", "action proposals", "COMPARE"]]