Active Object Localization
2 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers with no code
Leveraging Next-Active Objects for Context-Aware Anticipation in Egocentric Videos
Compared to existing video modeling architectures for action anticipation, NAOGAT captures the relationship between objects and the global scene context in order to predict detections for the next active object and anticipate relevant future actions given these detections, leveraging the objects' dynamics to improve accuracy.
Gaussian Processes with Context-Supported Priors for Active Object Localization
Next, we use a Gaussian Process to model this offset response signal over the search space of the target.
Collaborative Deep Reinforcement Learning for Joint Object Search
We examine the problem of joint top-down active search of multiple objects under interaction, e. g., person riding a bicycle, cups held by the table, etc..
Fast On-Line Kernel Density Estimation for Active Object Localization
In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations---a situation model---that represent the expected spatial structure of the given situation.
Active Object Localization in Visual Situations
We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization.