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Found 14 papers, 5 papers with code
Proposal-based Video Completion
Video inpainting is an important technique for a wide variety of applications from video content editing to video restoration.
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance.
Ranked #1 on
Image Classification
on iNaturalist 2019
(using extra training data)
Occupancy Planes for Single-view RGB-D Human Reconstruction
Specifically, a set of 3D locations within the view-frustum of the camera are first projected independently onto the image and a corresponding feature is subsequently extracted for each 3D location.
Equivariance Discovery by Learned Parameter-Sharing
Designing equivariance as an inductive bias into deep-nets has been a prominent approach to build effective models, e. g., a convolutional neural network incorporates translation equivariance.
Total Variation Optimization Layers for Computer Vision
To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision.
SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data
Moreover, existing image-based datasets for mesh reconstruction don't permit to study models which integrate temporal information.
Chirality Nets for Human Pose Regression
We propose Chirality Nets, a family of deep nets that is equivariant to the "chirality transform," i. e., the transformation to create a chiral pair.
SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation - A Synthetic Dataset and Baselines
We introduce SAIL-VOS (Semantic Amodal Instance Level Video Object Segmentation), a new dataset aiming to stimulate semantic amodal segmentation research.
Max-Sliced Wasserstein Distance and its use for GANs
Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning.
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
We even demonstrate competitive results comparable to deep learning based methods in the semi-supervised setting on the DAVIS dataset.
Ranked #3 on
Video Salient Object Detection
on DAVSOD-Difficult20
(using extra training data)
VideoMatch: Matching based Video Object Segmentation
Due to the formulation as a prediction task, most of these methods require fine-tuning during test time, such that the deep nets memorize the appearance of the objects of interest in the given video.
MaskRNN: Instance Level Video Object Segmentation
Instance level video object segmentation is an important technique for video editing and compression.
Progressive Feature Matching With Alternate Descriptor Selection and Correspondence Enrichment
We address two difficulties in establishing an accurate system for image matching.
Descriptor Ensemble: An Unsupervised Approach to Descriptor Fusion in the Homography Space
Inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain, our approach stands on the unsupervised nature of feature matching, and can select a good descriptor for matching each feature point.
