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:

Video provides not only rich visual cues such as motion and appearance, but also much less explored long-range temporal interactions among objects. We aim to capture such interactions and to construct a powerful intermediate-level video representation for subsequent recognition. Motivated by this goal, we seek to obtain spatio-temporal over-segmentation of a video into regions that respect object boundaries and, at the same time, associate object pix-els over many video frames. The contributions of this paper are twofold. First, we develop an efficient spatio-temporal video segmentation algorithm, which naturally incorporates long-range motion cues from the past and future frames in the form of clusters of point tracks with coherent motion. Second, we devise a new track clustering cost function that includes occlusion reasoning, in the form of depth ordering constraints, as well as motion similarity along the tracks. We evaluate the proposed approach on a challenging set of video sequences of office scenes from feature length movies.
[["motion", "appearance", "CONJUNCTION"], ["long-range motion cues", "spatio-temporal video segmentation algorithm", "USED-FOR"], ["video sequences of office scenes", "approach", "EVALUATE-FOR"], ["intermediate-level video representation", "recognition", "USED-FOR"], ["appearance", "visual cues", "HYPONYM-OF"], ["occlusion reasoning", "track clustering cost function", "PART-OF"], ["depth ordering constraints", "occlusion reasoning", "FEATURE-OF"], ["motion similarity", "track clustering cost function", "PART-OF"], ["visual cues", "Video", "FEATURE-OF"], ["motion", "visual cues", "HYPONYM-OF"], ["clusters of point tracks", "long-range motion cues", "USED-FOR"]]