Search Results for author:
Found 15 papers, 11 papers with code
Self-Supervised Burst Super-Resolution
Extensive experiments on real and synthetic data show that, despite only using noisy bursts during training, models trained with our self-supervised strategy match, and sometimes surpass, the quality of fully-supervised baselines trained with synthetic data or weakly-paired ground-truth.
Fast Hierarchical Learning for Few-Shot Object Detection
These approaches however suffer from ``catastrophic forgetting'' issue due to finetuning of base detector, leading to sub-optimal performance on the base classes.
Transforming Model Prediction for Tracking
Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function.
Ranked #20 on
Visual Object Tracking
on LaSOT
(Precision metric)
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction.
Ranked #4 on
Burst Image Super-Resolution
on BurstSR
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
Deep Burst Super-Resolution
We propose a novel architecture for the burst super-resolution task.
Ranked #6 on
Burst Image Super-Resolution
on SyntheticBurst
Burst Image Super-Resolution
Multi-Frame Super-Resolution
+1
Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
This effectively limits the performance and generalization capabilities of existing video segmentation methods.
Learning What to Learn for Video Object Segmentation
This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach.
Know Your Surroundings: Exploiting Scene Information for Object Tracking
Such approaches are however prone to fail in case of e. g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking.
Ranked #8 on
Object Tracking
on FE108
Energy-Based Models for Deep Probabilistic Regression
In our proposed approach, we create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x, y).
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
Learning Discriminative Model Prediction for Tracking
The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking.
Ranked #5 on
Object Tracking
on FE108
ATOM: Accurate Tracking by Overlap Maximization
We argue that this approach is fundamentally limited since target estimation is a complex task, requiring high-level knowledge about the object.
Ranked #7 on
Object Tracking
on FE108
Unveiling the Power of Deep Tracking
In the field of generic object tracking numerous attempts have been made to exploit deep features.
Deep Projective 3D Semantic Segmentation
Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results.
Ranked #15 on
Semantic Segmentation
on Semantic3D
ECO: Efficient Convolution Operators for Tracking
Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65. 0% AUC on OTB-2015.
Ranked #13 on
Visual Object Tracking
on VOT2017/18
