Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning

Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly supervised learning. In this case, the supervised information is restricted to binary labels that indicate the absence/presence of object instances in the image, without their locations. We follow a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images. Our main contribution is a multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations. This procedure is particularly important when using high-dimensional representations, such as Fisher vectors and convolutional neural network features. We also propose a window refinement method, which improves the localization accuracy by incorporating an objectness prior. We present a detailed experimental evaluation using the PASCAL VOC 2007 dataset, which verifies the effectiveness of our approach. © 2016 IEEE

On recognizing actions in still images via multiple features

We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant to the actions. We test our method on the extensive Stanford 40 Actions dataset [1] and achieve significant performance gain compared to the state-of-the-art. Our results show that using multiple object hypotheses within multiple instance learning is effective for human action recognition in still images and such an object representation is suitable for using in conjunction with other visual features. © 2012 Springer-Verlag

Contextual Object Detection with a Few Relevant Neighbors

A natural way to improve the detection of objects is to consider the contextual constraints imposed by the detection of additional objects in a given scene. In this work, we exploit the spatial relations between objects in order to improve detection capacity, as well as analyze various properties of the contextual object detection problem. To precisely calculate context-based probabilities of objects, we developed a model that examines the interactions between objects in an exact probabilistic setting, in contrast to previous methods that typically utilize approximations based on pairwise interactions. Such a scheme is facilitated by the realistic assumption that the existence of an object in any given location is influenced by only few informative locations in space. Based on this assumption, we suggest a method for identifying these relevant locations and integrating them into a mostly exact calculation of probability based on their raw detector responses. This scheme is shown to improve detection results and provides unique insights about the process of contextual inference for object detection. We show that it is generally difficult to learn that a particular object reduces the probability of another, and that in cases when the context and detector strongly disagree this learning becomes virtually impossible for the purposes of improving the results of an object detector. Finally, we demonstrate improved detection results through use of our approach as applied to the PASCAL VOC and COCO datasets

A migraine variant with abdominal colic and Alice in wonderland syndrome: a case report and review

<p>Abstract</p> <p>Background</p> <p>Abdominal migraine is a commonly described migraine variant in children and young adults, but associations with Alice in Wonderland syndrome and lilliputian hallucinations are exceptional.</p> <p>Case presentation</p> <p>A 20 years-old male experienced frequent and prolonged attacks of abdominal colic associated with autonomic manifestations started at the age of ten. At the age of 17, he additionally described prolonged attacks (≥ 7 days) of distortions of shape, size or position of objects or subjects. He said "Quite suddenly, objects appear small and distant (teliopsia) or large and close (peliopsia). I feel as I am getting shorter and smaller "shrinking" and also the size of persons are not longer than my index finger (a lilliputian proportion). Sometimes I see the blind in the window or the television getting up and down, or my leg or arm is swinging. I may hear the voices of people quite loud and close or faint and far. Occasionally, I experience attacks of migrainous headache associated with eye redness, flashes of lights and a feeling of giddiness. I am always conscious to the intangible changes in myself and my environment". There is a strong family history of common migraine. Clinical examination, brain-MRI and EEG were normal. Transcranial magnetic stimulation and evoked potentials revealed enhanced cortical excitability in multiple brain regions. Treatment with valproate resulted in marked improvement of all clinical and neurophysiological abnormalities.</p> <p>Conclusions</p> <p>The association between the two migraine variants (abdominal migraine and Alice in Wonderland Syndrome) might have clinical, pathophysiological and management implications. I think this is the first description in the literature.</p

Recognizing Complex Events in Videos by Learning Key Static-Dynamic Evidences

Abstract. Complex events consist of various human interactions with different objects in diverse environments. The evidences needed to rec-ognize events may occur in short time periods with variable lengths and can happen anywhere in a video. This fact prevents conventional machine learning algorithms from effectively recognizing the events. In this pa-per, we propose a novel method that can automatically identify the key evidences in videos for detecting complex events. Both static instances (objects) and dynamic instances (actions) are considered by sampling frames and temporal segments respectively. To compare the character-istic power of heterogeneous instances, we embed static and dynamic instances into a multiple instance learning framework via instance simi-larity measures, and cast the problem as an Evidence Selective Ranking (ESR) process. We impose 1 norm to select key evidences while us-ing the Infinite Push Loss Function to enforce positive videos to have higher detection scores than negative videos. The Alternating Direction Method of Multipliers (ADMM) algorithm is used to solve the optimiza-tion problem. Experiments on large-scale video datasets show that our method can improve the detection accuracy while providing the unique capability in discovering key evidences of each complex event