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Found 9 papers, 2 papers with code
LoMOE: Localized Multi-Object Editing via Multi-Diffusion
Our experiments against existing state-of-the-art methods demonstrate the improved effectiveness of our approach in terms of both image editing quality and inference speed.
Transfer4D: A Framework for Frugal Motion Capture and Deformation Transfer
Animating a virtual character based on a real performance of an actor is a challenging task that currently requires expensive motion capture setups and additional effort by expert animators, rendering it accessible only to large production houses.
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning
Contemporary model calibration techniques mitigate the problem of overconfident predictions by pushing down the confidence of the winning class while increasing the confidence of the remaining classes across all test samples.
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
We show that training with MDCA leads to better-calibrated models in terms of Expected Calibration Error ( ECE ), and Static Calibration Error ( SCE ) on image classification, and segmentation tasks.
PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation
While Out-of-distribution (OOD) detection has been well explored in computer vision, there have been relatively few prior attempts in OOD detection for NLP classification.
Synthetic Video Generation for Robust Hand Gesture Recognition in Augmented Reality Applications
The goal of this work is to introduce a framework capable of generating photo-realistic videos that have labelled hand bounding box and fingertip that can help in designing, training, and benchmarking models for hand-gesture recognition in AR/VR applications.
Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework
We demonstrate that the marriage of KD and the VI techniques inherits compression properties from the KD framework, and enhances levels of sparsity from the VI approach, with minimal compromise in the model accuracy.
GestARLite: An On-Device Pointing Finger Based Gestural Interface for Smartphones and Video See-Through Head-Mounts
The overall framework works in real-time on mobile devices and achieves a classification accuracy of 80% on EgoGestAR video dataset with an average latency of only 0. 12 s.
AirPen: A Touchless Fingertip Based Gestural Interface for Smartphones and Head-Mounted Devices
Hand gestures are an intuitive, socially acceptable, and a non-intrusive interaction modality in Mixed Reality (MR) and smartphone based applications.
Human-Computer Interaction
