Ivan Krasin
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View article: RobustNeRF: Ignoring Distractors with Robust Losses
RobustNeRF: Ignoring Distractors with Robust Losses Open
Neural radiance fields (NeRF) excel at synthesizing new views given multi-view, calibrated images of a static scene. When scenes include distractors, which are not persistent during image capture (moving objects, lighting variations, shado…
View article: Transporter Networks: Rearranging the Visual World for Robotic Manipulation
Transporter Networks: Rearranging the Visual World for Robotic Manipulation Open
Robotic manipulation can be formulated as inducing a sequence of spatial displacements: where the space being moved can encompass an object, part of an object, or end effector. In this work, we propose the Transporter Network, a simple mod…
View article: The Open Images Dataset V4
The Open Images Dataset V4 Open
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adap…
View article: The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale Open
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adap…
View article: The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale Open
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adap…
View article: Learning From Noisy Large-Scale Datasets With Minimal Supervision
Learning From Noisy Large-Scale Datasets With Minimal Supervision Open
We present an approach to effectively use millions of images with noisy annotations in conjunction with a small subset of cleanly-annotated images to learn powerful image representations. One common approach to combine clean and noisy data…