Multiple Instance Curriculum Learning for Weakly Supervised Object Detection

When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes. The training set is gradually expanded to include harder examples to train strong detectors that handle complex images. The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201

Impaired thermoregulatory function during dynamic exercise in multiple sclerosis

INTRODUCTION: Impairments in sudomotor function during passive whole-body heating have been reported in multiple sclerosis (MS), a demyelinating disease of the CNS that disrupts autonomic function. However, the capability of the thermoregulatory system to control body temperature during exercise has never been assessed in MS. Thus, the aim of the present study was to test the hypothesis that thermoregulatory function is impaired in MS patients compared to healthy controls (CON) exercising at similar rates of metabolic heat production. METHODS: Sweating and skin blood flow responses were compared between 12 individuals diagnosed with relapsing-remitting MS (9 females, 3 males) and 12 sex-, age-, mass- and BSA-matched healthy controls during a single bout of cycling exercise (rate of metabolic heat production: ~4.5 W/kg) for 60 min in a climate-controlled room (25 °C, 30% RH). RESULTS: Individuals with MS exhibited an attenuated increase in cumulative whole-body sweat loss after 30 min (MS: 72 ± 51; CON: 104 ± 37 g, p=0.04) and 60 min (MS: 209 ± 94; CON: 285 ± 62 g, p=0.02), as well as lower sweating thermosensitivity (MS: 0.49 ± 0.26; CON: 0.86 ± 0.30 mg/cm2/min/°C, p=0.049). Despite evidence for thermoregulatory dysfunction, there were no differences between MS and CON in esophageal or rectal temperatures at 30 or 60 min time points (p>0.05). Cutaneous vasculature responses were also not different in MS compared to CON (p>0.05). CONCLUSION: Taken together, MS blunts sweating responses during exercise while cutaneous vasculature responses are preserved. Altered mechanisms of body temperature regulation in persons with MS may lead to temporary worsening of disease symptoms and limit exercise tolerance under more thermally challenging conditions.Accepted manuscrip

Programming with heterogeneous structures: Manipulating XML data using bondi

Manipulating semistructured data, such as XML, does not fit well within conventional programming languages. A typical manipulation requires finding all occurrences of a structure matching a structured search pattern, whose context may be different in different places, and both aspects cause difficulty. If a special-purpose query language is used to manipulate XML, an interface to a more general programming environment is required, and this interface typically creates runtime overhead for type conversion. However, adding XML manipulation to a general-purpose programming language has proven difficult because of problems associated with expressiveness and typing. We show an alternative approach that handles many kinds of patterns within an existing strongly-typed general-purpose programming language called bondi. The key ideas are to express complex search patterns as structures of simple patterns, pass these complex patterns as parameters to generic data-processing functions and traverse heterogeneous data structures by a generalized form of pattern matching. These ideas are made possible by the language's support for pattern calculus, whose typing on structures and patterns enables path and pattern polymorphism. With this approach, adding a new kind of pattern is just a matter of programming, not language design. Copyright © 2006, Australian Computer Society, Inc