In this work, we study the effect of occlusion on video action recognition.

Visual question answering ( VQA) is a challenging task that requires an in-depth understanding of vision and language, as well as multi-modal reasoning.

In our case, it boils down to: ' The smoothing effect induced by the average helps to reduce the visual noise, and hence improves the explanations. For the experiment, m and are the same as SmoothGrad. We start by deriving the closed form of Saliency (SA) and naturally Gradient-Input (GI): ' The case of V arGrad is specific, as the gradient of a linear system being constant, its variance is null. W We recall that for Gradient Input, Integrated Gradients, Occlusion, ' It was quickly realized that they unified properties of various domains such as graph theory, linear algebra or geometry. Later, in the '60s, a connection was made At each step, the insertion metric selects the concepts of maximum score given a cardinality constraint.

We thank the reviewers for their valuable comments. We are glad that reviewers noted our paper as novel (R1: "idea is "Decouple the effect of capacity increase and curriculum learning": We would like to We will also move related works section as suggested. We agree that this issue is important in the field of curriculum learning. "It could be interesting to show results on the large W ebVision Benchmark": "W ould proposed curriculum change robustness to adversarial attacks": On average, our method requires 20 % fewer epochs. ImageNet, we conducted new experiments on WebVision dataset (2.3 million training images) and obtain significant Please see the first table above.

While implicit function methods capture detailed clothed shapes, they require aligned shape priors and or are weak at inpainting occluded regions given an image input.

This paper introduces $\textit{Bifröst}$, a novel 3D-aware framework that is built upon diffusion models to perform instruction-based image composition. Previous methods concentrate on image compositing at the 2D level, which fall short in handling complex spatial relationships ($\textit{e.g.}$, occlusion).

In this work, we study the effect of occlusion on video action recognition. Tofacilitate this study, we propose three benchmark datasets and experiment withseven different video action recognition models. These datasets include two synthetic benchmarks, UCF-101-O and K-400-O, which enabled understanding the effects of fundamental properties of occlusion via controlled experiments. We also propose a real-world occlusion dataset, UCF-101-Y-OCC, which helps in further validating the findings of this study. We find several interesting insights such as 1) transformers are more robust than CNN counterparts, 2) pretraining make modelsrobust against occlusions, and 3) augmentation helps, but does not generalize well to real-world occlusions. In addition, we propose a simple transformer based compositional model, termed as CTx-Net, which generalizes well under this distribution shift. We observe that CTx-Net outperforms models which are trained using occlusions as augmentation, performing significantly better under natural occlusions.