24,971 research outputs found
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
Importance of visual context in scene understanding tasks is well recognized
in the computer vision community. However, to what extent the computer vision
models for image classification and semantic segmentation are dependent on the
context to make their predictions is unclear. A model overly relying on context
will fail when encountering objects in context distributions different from
training data and hence it is important to identify these dependencies before
we can deploy the models in the real-world. We propose a method to quantify the
sensitivity of black-box vision models to visual context by editing images to
remove selected objects and measuring the response of the target models. We
apply this methodology on two tasks, image classification and semantic
segmentation, and discover undesirable dependency between objects and context,
for example that "sidewalk" segmentation relies heavily on "cars" being present
in the image. We propose an object removal based data augmentation solution to
mitigate this dependency and increase the robustness of classification and
segmentation models to contextual variations. Our experiments show that the
proposed data augmentation helps these models improve the performance in
out-of-context scenarios, while preserving the performance on regular data.Comment: 14 pages (12 figures
PanDA: Panoptic Data Augmentation
The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data augmentation (PanDA) method which operates exclusively in pixel space, requires no additional data or training, and is computationally cheap to implement. By retraining original state-of-the-art models on PanDA augmented datasets generated with a single frozen set of parameters, we show robust performance gains in panoptic segmentation, instance segmentation, as well as detection across models, backbones, dataset domains, and scales. Finally, the effectiveness of unrealistic-looking training images synthesized by PanDA suggest that one should rethink the need for image realism for efficient data augmentation
A computational approach for obstruction-free photography
We present a unified computational approach for taking photos through reflecting or occluding elements such as windows and fences. Rather than capturing a single image, we instruct the user to take a short image sequence while slightly moving the camera. Differences that often exist in the relative position of the background and the obstructing elements from the camera allow us to separate them based on their motions, and to recover the desired background scene as if the visual obstructions were not there. We show results on controlled experiments and many real and practical scenarios, including shooting through reflections, fences, and raindrop-covered windows.Shell ResearchUnited States. Office of Naval Research (Navy Fund 6923196
- …