DVP+ extends a single-object disentangling autoencoder to multi-object scenes, using alternative training losses and per-scene optimization to outperform MONet and LIVE on a new benchmark, when the object count is known.
1), aim to select the optimal match between object candidates and ground truth objects on translation and color
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Generative Learning of Differentiable Object Models for Compositional Interpretation of Complex Scenes
DVP+ extends a single-object disentangling autoencoder to multi-object scenes, using alternative training losses and per-scene optimization to outperform MONet and LIVE on a new benchmark, when the object count is known.