Discriminatively Trained Latent Ordinal Model for Video Classification
نویسندگان
چکیده
منابع مشابه
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and ...
متن کاملDiscriminatively trained phoneme confusion model for keyword spotting
Keyword Spotting (KWS) aims at detecting speech segments that contain a given query within large amounts of audio data. Typically, a speech recognizer is involved in a first indexing step. One of the challenges of KWS is how to handle recognition errors and out-of-vocabulary (OOV) terms. This work proposes the use of discriminative training to construct a phoneme confusion model, which expands ...
متن کاملA Preference Ranking Model Using a Discriminatively-trained Classifier
This paper presents an ordinal regression approach to the query-by-description problem. Instead of returning a single classification, such as genre, or a list of the top N songs assumed to be relevant, this algorithm mirrors choices similar to "like", "skip", "play", and "hate" buttons seen on commercial Internet radio stations. Ordinal regression can be viewed as a hybrid between multi-class c...
متن کاملDiscriminatively Trained Dense Surface Normal Estimation
In this work we propose the method for a rather unexplored problem of computer vision discriminatively trained dense surface normal estimation from a single image. Our method combines contextual and segment-based cues and builds a regressor in a boosting framework by transforming the problem into the regression of coefficients of a local coding. We apply our method to two challenging data sets ...
متن کاملDiscriminatively Trained Mixtures of Deformable Part Models
We have developed a new system building on our work on discriminatively trained, multiscale, deformable part models [1]. As in our previous system the models are trained using a discriminative procedure that only requires bounding box labels for positive examples. Our new system uses mixture models. Each mixture component is similar to a model from [1], consisting of a coarse “root” filter and ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2018
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2017.2741482