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20152026
most citedLoss Functions for Neural Networks for Image Processing

224 citations · 353 across the 25 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.CV2021

Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects

Atsuhiro Noguchi, Umar Iqbal, Jonathan Tremblay +2

Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies. Manipulating the pose of an object, however…

cs.CV2021★ 46 cited

Efficient Geometry-aware 3D Generative Adversarial Networks

Eric R. Chan, Connor Z. Lin, Matthew A. Chan +9

Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge. Existing…

cs.CV2021★ 25 cited

Neural Trajectory Fields for Dynamic Novel View Synthesis

Chaoyang Wang, Ben Eckart, Simon Lucey +1

Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes. The ability to rec…

cs.CV2021

Noise-Aware Video Saliency Prediction

Ekta Prashnani, Orazio Gallo, Joohwan Kim +3

We tackle the problem of predicting saliency maps for videos of dynamic scenes. We note that the accuracy of the maps reconstructed from the gaze data of a fixed number of observer…

cs.CV2021★ 2 cited

Binary TTC: A Temporal Geofence for Autonomous Navigation

Abhishek Badki, Orazio Gallo, Jan Kautz +1

Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocit…