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 propose a new approach to generate oriented object proposals (OOPs) to reduce the detection error caused by various orientations of the object. To this end, we propose to efficiently locate object regions according to pixelwise object probability, rather than measuring the objectness from a set of sampled windows. We formulate the proposal generation problem as a generative proba-bilistic model such that object proposals of different shapes (i.e., sizes and orientations) can be produced by locating the local maximum likelihoods. The new approach has three main advantages. First, it helps the object detector handle objects of different orientations. Second, as the shapes of the proposals may vary to fit the objects, the resulting proposals are tighter than the sampling windows with fixed sizes. Third, it avoids massive window sampling, and thereby reducing the number of proposals while maintaining a high recall. Experiments on the PASCAL VOC 2007 dataset show that the proposed OOP outperforms the state-of-the-art fast methods. Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art proposal generation methods in either object rotation scenarios or general scenarios. Generating OOPs is very fast and takes only 0.5 s per image.
[["object rotation scenarios", "proposal generation methods", "EVALUATE-FOR"], ["sizes", "shapes", "HYPONYM-OF"], ["PASCAL VOC 2007 dataset", "OOP", "EVALUATE-FOR"], ["OOP", "state-of-the-art fast methods", "COMPARE"], ["approach", "number of proposals", "USED-FOR"], ["orientations", "shapes", "HYPONYM-OF"], ["rotation invariant property", "class-specific object detector", "USED-FOR"], ["object detector", "orientations", "USED-FOR"], ["shapes", "object proposals", "FEATURE-OF"], ["detection error", "oriented object proposals (OOPs)", "EVALUATE-FOR"], ["recall", "approach", "EVALUATE-FOR"], ["class-specific object detector", "proposal generation methods", "COMPARE"], ["local maximum likelihoods", "object proposals", "USED-FOR"], ["approach", "oriented object proposals (OOPs)", "USED-FOR"], ["general scenarios", "proposal generation methods", "EVALUATE-FOR"], ["approach", "proposal generation problem", "USED-FOR"], ["sizes", "orientations", "CONJUNCTION"], ["pixelwise object probability", "object regions", "USED-FOR"], ["general scenarios", "class-specific object detector", "EVALUATE-FOR"], ["object rotation scenarios", "general scenarios", "CONJUNCTION"], ["pixelwise object probability", "objectness", "COMPARE"], ["object rotation scenarios", "class-specific object detector", "EVALUATE-FOR"]]