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Found 11 papers, 4 papers with code
Finding Waldo: Towards Efficient Exploration of NeRF Scene Spaces
Neural Radiance Fields (NeRF) have quickly become the primary approach for 3D reconstruction and novel view synthesis in recent years due to their remarkable performance.
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation
To this end, we design a small, modular, and efficient (just 2GFLOPs to process a Full HD image) system to enhance input data for robust downstream multimedia understanding with minimal computational cost.
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness
First, inspired by these observations, we propose a simple yet effective data augmentation method HybridAugment that reduces the reliance of CNNs on high-frequency components, and thus improves their robustness while keeping their clean accuracy high.
TrickVOS: A Bag of Tricks for Video Object Segmentation
Space-time memory (STM) network methods have been dominant in semi-supervised video object segmentation (SVOS) due to their remarkable performance.
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation
This paper tackles the problem of semi-supervised video object segmentation on resource-constrained devices, such as mobile phones.
Ranked #6 on
Video Object Segmentation
on YouTube-VOS 2019
Adaptive Mask-based Pyramid Network for Realistic Bokeh Rendering
Bokeh effect highlights an object (or any part of the image) while blurring the rest of the image, and creates a visually pleasant artistic effect.
How Robust are Discriminatively Trained Zero-Shot Learning Models?
In this paper, we present novel analyses on the robustness of discriminative ZSL to image corruptions.
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile
Increasingly accurate models typically require more computational resources, which inhibits the use of such models on mobile devices.
Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos
To mimic such scenarios, we formulate a realistic domain-transfer problem, where the goal is to transfer the recognition model trained on clean posed images to the target domain of violent videos, where training videos are available only for a subset of subjects.
A Deep Dive into Adversarial Robustness in Zero-Shot Learning
In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes.
Wildest Faces: Face Detection and Recognition in Violent Settings
With the introduction of large-scale datasets and deep learning models capable of learning complex representations, impressive advances have emerged in face detection and recognition tasks.
