Competitors We compared the proposed SLST model with five state-of-the-art alternative detection approaches: (1) Faster R-CNN [24]: A competitive region proposal driven object detection model which is characterised by jointly learning region proposal generation and object classification in a single deep model In our scalable webly learning context, the Faster R-CNN is optimised with synthetic training data generated by the SCL [32] method, exactly the same as our SLST model.
Sample text:```…(LING)] thalamus (THA), as well as limbic/paralimbic regions [e.g. insula, superior temporal pole and inferior orbitofrontal cortex] (Table 2), which is in accordance with previous findings on functional brain networks constructed using resting-state fMRI (Achard et al., 2006; He et al., 2009b).```
Sample intent: result
Sample text:```…(LING)] thalamus (THA), as well as limbic/paralimbic regions [e.g. insula, superior temporal pole and inferior orbitofrontal cortex] (Table 2), which is in accordance with previous findings on functional brain networks constructed using resting-state fMRI (Achard et al., 2006; He et al., 2009b).```
Sample intent: result



Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
Sample intent: method



Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
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Sample text:```In this study, the proposed method is based on atrial activity extraction using an echo state network (ESN) re­ cently introduced as a unified solution to the problem of QRST cancellation in the presence of substantial variation in beat morphology and/or occasional ectopic beats [7].```
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Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result
Sample text:```…methods rely on some additional and often heuristic step to generate an initial solution, such as adversarially-trained neural networks [Conneau et al., 2018, Zhang et al., 2017a,b], robust self-learning [Artetxe et al., 2018b] or
5This can be easily verified by noting that the…```
Sample intent: background



Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
Sample intent: method



Sample text:```…(LING)] thalamus (THA), as well as limbic/paralimbic regions [e.g. insula, superior temporal pole and inferior orbitofrontal cortex] (Table 2), which is in accordance with previous findings on functional brain networks constructed using resting-state fMRI (Achard et al., 2006; He et al., 2009b).```
Sample intent: result



Sample text:```…with several action-based models of selective action that suggest turning one’s visual attention to an object or location in space may be the same process as preparing a movement to that object/location (e.g., Rizzolatti et al.1994; Tipper et al.1992; Welsh and Elliott 2004b; Welsh et al.1999).```
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Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
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Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
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Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
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Sample text:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
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Sample text:```…between output and input, CNF achieves almost 10% relative improvement over CRF. CNF also outperforms other methods including SVMpro (Hua and Sun, 2001), SVMpsi (Kim and Park, 2003), YASSPP (Karypis, 2006), PSIPRED (Jones, 1999), SPINE (Dor and Zhou, 2007) and TreeCRFpsi (Dietterich et al., 2004).```
Sample intent: background