3260 papers • 126 benchmarks • 313 datasets
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This work designs a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference in structured prediction tasks such as articulated pose estimation.
This work presents a general framework that jointly detects and forms spatio-temporal keypoint associations in a single stage, making this the first real-time pose detection and tracking algorithm.
A fast bottom-up method that jointly detects over 100 keypoints on humans or objects, also referred to as human/object pose estimation, and uses a graph centrality measure to assign training weights to different parts of a pose.
This work proposes integrating Markovian Structural Bias, which modulates the self-attention interaction between nodes based on the number of hops between them, and shows that this improves the model's ability to capture global spatial dependencies.
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