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A discriminative learning framework with pairwise constraints for video object classification

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Image Parsing: Unifying Segmentation, Detection, and Recognition
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Image Parsing: Unifying Segmentation, Detection, and Recognition
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Face detection with information-based maximum discrimination
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Integrated face and gait recognition from multiple views
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Object class recognition by unsupervised scale-invariant learning
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Enhancing image and video retrieval: learning via equivalence constraints
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A discriminative learning framework with pairwise constraints for video object classi .cation
inferredcites →https://doi.org/10.1109/cvpr.2004.440openalex-snapshot
Slightly Supervised Learning of Part-Based Appearance Models
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Learning with Constrained and Unlabelled Data
inferredcites →https://doi.org/10.1109/iccv.2003.1238391openalex-snapshot
Automatically labeling video data using multi-class active learning
inferredcites →https://doi.org/10.1109/iccv.2003.1238422openalex-snapshot
Detecting pedestrians using patterns of motion and appearance
inferredcites →https://doi.org/10.1109/iccv.2003.1238476openalex-snapshot
A Bayesian approach to unsupervised one-shot learning of object categories
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Discriminative random fields: a discriminative framework for contextual interaction in classification
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Video Google: a text retrieval approach to object matching in videos
inferredcites →https://doi.org/10.1109/tpami.2006.65openalex-snapshot
A discriminative learning framework with pairwise constraints for video object classification
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Atomatic target recognition (ATR) evaluation theory: a survey
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An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds
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Active Semi-Supervision for Pairwise Constrained Clustering
inferredcites →https://doi.org/10.1145/1014052.1014062openalex-snapshot
A probabilistic framework for semi-supervised clustering
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Kernel Logistic Regression and the Import Vector Machine
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Fast Training of Support Vector Machines Using Sequential Minimal Optimization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Similarity beyond distance measurement
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