Melanin 发表于 2025-3-21 16:04:10
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Cascade-based Real-Time Face Detection, and Maydt, 2002) used manual tuning or heuristics to set the intermediate rejection thresholds for the detector, which is inefficient and suboptimal. Recently, various approaches has been proposed to address this issue. Notably, Bourdev and Brandt (Bourdev and Brandt, 2005) proposed a method for se争议的苹果 发表于 2025-3-22 03:31:30
Multiple Instance Learning for Face Detection,ion results surrounding the ground truth rectangle are plausible. Such an observation is indeed quite general. In many object recognition tasks, it is often extremely tedious to generate large training sets of objects because it is not easy to specify exactly where the objects are. For instance, giv星球的光亮度 发表于 2025-3-22 04:47:53
Detector Adaptation,l-known that the performance of such a learned classifier will depend heavily on the representativeness of the labeled data used during training. If the training data contains only a small number of examples sampled in a particular test environment, the learned classifier may be too specific to be gWITH 发表于 2025-3-22 10:01:48
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LBS and TeleCartography II: About the bookWe have focused on face detection almost exclusively in the previous chapters. In this chapter, we will present two other applications of boosting learning. These two applications extend the above algorithms in two ways: the learning algorithm itself, and the features being used for learning.Allowance 发表于 2025-3-22 20:16:45
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Conclusions and FutureWork,ne learning literature, such as the confidence rated boosting (Schapire and Singer, 1999), the statistical view of boosting (Friedman et al., 1998), the AnyBoost framework (Mason et al., 2000), which views boosting as a gradient decent process, and the general idea of multiple instance learning (Nowlan and Platt, 1995).Acetaminophen 发表于 2025-3-23 03:12:05
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