While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing.

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Image matting is an ill-posed problem. It requires a user input trimap or some strokes to obtain an alpha matte of the foreground object.

The second factor is the availability of big data, which oftentimes contain private and sensitive information.

Video salient object ranking aims to simulate the human attention mechanism by dynamically prioritizing the visual attraction of objects in a scene over time. Despite itsnumerous practical applications, this area remains underexplored. In this work, we propose a graph model for video salient object ranking.

Gating, as used in gated recurrent units (GRUs) [4] and long short-term memory (LSTM) networks [12], has become common-place in recurrent architectures. Gates facilitate the learning of longer term temporal relationships [12]. Furthermore, in the presence of noise in the input signal, gates can protect the cell state from undesired updates, thereby improving overall stability and convergence.

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