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Found 6 papers, 2 papers with code
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types
Our study leads to several insights and concrete recommendations: (1) for most tasks there exists a source which significantly outperforms ILSVRC'12 pre-training; (2) the image domain is the most important factor for achieving positive transfer; (3) the source dataset should \emph{include} the image domain of the target dataset to achieve best results; (4) at the same time, we observe only small negative effects when the image domain of the source task is much broader than that of the target; (5) transfer across task types can be beneficial, but its success is heavily dependent on both the source and target task types.
The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
We present Open Images V4, a dataset of 9. 2M images with unified annotations for image classification, object detection and visual relationship detection.
Detecting Visual Relationships Using Box Attention
We propose a new model for detecting visual relationships, such as "person riding motorcycle" or "bottle on table".
Expanding Object Detector's Horizon: Incremental Learning Framework for Object Detection in Videos
By incrementally detecting object instances in video and adding confident detections into the model, we are able to dynamically adjust the complexity of the detector over time by instantiating new prototypes to span all domains the model has seen.
Learning an Image-based Motion Context for Multiple People Tracking
We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals.
