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Found 24 papers, 10 papers with code
MoViNets: Mobile Video Networks for Efficient Video Recognition
We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.
Ranked #3 on
Action Classification
on Charades
Unsupervised Learning of Depth and Ego-Motion from Video
We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences.
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning.
AirSim Drone Racing Lab
Autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control.
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model to perform well on all classes.
Ranked #27 on
Long-tail Learning
on Places-LT
2.5D Visual Relationship Detection
To enable progress on this task, we create a new dataset consisting of 220k human-annotated 2. 5D relationships among 512K objects from 11K images.
Federated Visual Classification with Real-World Data Distribution
Furthermore, differing quantities of data are typically available at each device (imbalance).
Decision Forests, Convolutional Networks and the Models in-Between
We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.
Low-Shot Learning with Imprinted Weights
We call this process weight imprinting as it directly sets weights for a new category based on an appropriately scaled copy of the embedding layer activations for that training example.
Frame-Recurrent Video Super-Resolution
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images.
Ranked #6 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
Learning to Segment via Cut-and-Paste
This paper presents a weakly-supervised approach to object instance segmentation.
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks
Deep reinforcement learning yields great results for a large array of problems, but models are generally retrained anew for each new problem to be solved.
Pose2Instance: Harnessing Keypoints for Person Instance Segmentation
In a more realistic environment, without the oracle keypoints, the proposed deep person instance segmentation model conditioned on human pose achieves 3. 8% to 10. 5% relative improvements comparing with its strongest baseline of a deep network trained only for segmentation.
Nonrigid Optical Flow Ground Truth for Real-World Scenes with Time-Varying Shading Effects
In this paper we present a dense ground truth dataset of nonrigidly deforming real-world scenes.
Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences
We demonstrate the success of our approach by showing significant error reduction on 6 popular optical flow algorithms applied to a range of real-world nonrigid benchmarks.
Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?
In the extreme, we observed that a model trained on patches extracted from just one scan, with each patch augmented 50 times; achieved a Dice score of 0. 73 in a validation set of 40 cases.
Optical Flow Estimation Using Laplacian Mesh Energy
In this paper we present a novel non-rigid optical flow algorithm for dense image correspondence and non-rigid registration.
Learning Similarity Metrics for Dynamic Scene Segmentation
This paper addresses the segmentation of videos with arbitrary motion, including dynamic textures, using novel motion features and a supervised learning approach.
Enhancing Video Summarization via Vision-Language Embedding
This paper addresses video summarization, or the problem of distilling a raw video into a shorter form while still capturing the original story.
When Ensembling Smaller Models is More Efficient than Single Large Models
Ensembling is a simple and popular technique for boosting evaluation performance by training multiple models (e. g., with different initializations) and aggregating their predictions.
GeLaTO: Generative Latent Textured Objects
Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem.
FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling
We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images.
Exploring Temporal Granularity in Self-Supervised Video Representation Learning
The training objective consists of two parts: a fine-grained temporal learning objective to maximize the similarity between corresponding temporal embeddings in the short clip and the long clip, and a persistent temporal learning objective to pull together global embeddings of the two clips.
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text
The experiments show that the resultant unified foundation transformer works surprisingly well on both the vision-only and text-only tasks, and the proposed knowledge distillation and gradient masking strategy can effectively lift the performance to approach the level of separately-trained models.
