Overall dataset-specific architecture: The overall architecture equipped with all our datasetspecific modules is shown in Figure 1. We design the dataset-specific modules to be light-weight, which allows us to save on memory costs.

Due to error propagation among neighboring frames, more amount of corruption can be observed in the decoded frame sequences of each Figure.

The annotation tool is a free painting tool, which allows the raters to freely draw the instance mask. We ask the raters to try to draw within the bbox, but if the object is obviously exceeding the bbox, then they can draw outside the bbox. The size of the stroke is adjustable.

Cross-object occlusions remain an important source of failures for current state-of-the-art object detectors [29],which, despite their formidable performance increase inrecent years, still carrythe biases and idiosyncrasies of the data they were trained on [16]: static images from Imagenet and COCOdatasets.

For all other remaining architectures, the reported results are from private datasets. Neck Shaft Angle(NSA) cannot be estimated. Additionally, [? ] requires estimation of the diaphysis Figure 4: Repeatability of the femur morphometry extraction method as measured by error distributions for a) the landmarks/anatomical sizes and b) axis alignment identified by the adapted method. Do the main claims made in the abstract and introduction accurately reflect the paper's Did you specify all the training details (e.g., data splits, hyperparameters, how they were Data splits are available in the GitHub repository. Did you report error bars (e.g., with respect to the random seed after running ex-67 Did you include the total amount of compute and the type of resources used (e.g., Did you mention the license of the assets?

However,two ofthe most popular benchmarks donot provide the full training information for eacharchitecture.

All of the questions posed in Table 1in the main paper can be decomposed into readily available components that our modelpθ can estimate. Q1 P (X1) is already naturally in a form that our model can directly estimate due to the autoregressive factorization imposed by the architecture:p θ(X1). Q3 The "hitting time" or the next occurrence of a specific event typea V is defined asτ(a). Interestingly, we can see thatQ3 is a generalization ofQ2 by noting that they are identical when A={}. In practice, computing this exactly is intractable due to it being an infinite sum.

Most existing neural architecture search (NAS) algorithms are dedicated to and evaluated bythedownstream tasks, e.g.,image classification incomputer vision.