Each sublayer adds a skip-connection (ADD) and batch normalization (BN). The decoder sequentially chooses a node according to a probability distribution produced by the node embeddings to construct a solution. The scaled symmetric sampling method is shown in Algorithm 2. The scaled factor The uniform division of the weight space is illustrated as follows. Thus, its approximate Pareto optimal solutions are commonly pursued. V ehicles must serve all the customers and finally return to the depot.

Motivated by our analysis results, we propose new data augmentation methods aimed at enhancing model generalization.

An important step to understanding and improving artificial vision systems is to measure image similarity purely based on intrinsic object properties that define object identity. This problem has been studied in the computer vision literature as re-identification, though mostly restricted to specific object categories such as people and cars. We propose to extend it to general object categories, exploring an image similarity metric based on object intrinsics.

Neural Information Processing Systems http://nips.cc/

Recent development in vision-language approaches has instigated a paradigm shift in learning visual recognition models from language supervision. These approaches align objects with language queries (e.g. "a photo of a cat") and thus improve the models' adaptability to novel objects and domains.

Integrated development environments (IDEs) assist developers in understanding repository context using static analysis.

Farmworld is an open-ended gridworld environment designed with two goals in mind: high customiz-ability and support for diverse solutions. Maps can be hand-crafted, or randomly generated. RGB images are used, agents'see' exactly what we see: units visibly lose health by damage patterns Agents have partially-observable observations: they do not see the entire map. Reward Agents get an individual reward of 0.1 for each timestep that they are alive. To this end, we augmented DIA YN and called this DIA YN*. We subtract by the batch mean so that on average, the expected agent reward equals only what is provided by the extrinsic environment.

In this paper, we propose a novel benchmark, named VastTrack, aiming to facilitate the development of general visual tracking via encompassing abundant classes and videos. VastTrack consists of a few attractive properties: (1) Vast Object Category. In particular, it covers targets from 2,115 categories, significantly surpassing object classes of existing popular benchmarks (e.g., GOT-10k with 563 classes and LaSOT with 70 categories). Through providing such vast object classes, we expect to learn more general object tracking.