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Found 11 papers, 5 papers with code
Beyond SOT: It's Time to Track Multiple Generic Objects at Once
TaMOs achieves a 4x faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark.
AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects.
Robust Visual Tracking by Segmentation
We infer a bounding box from the segmentation mask, validate our tracker on challenging tracking datasets and achieve the new state of the art on LaSOT with a success AUC score of 69. 7%.
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 #1 on
Visual Object Tracking
on LaSOT
(IS metric)
Best Practices in Pool-based Active Learning for Image Classification
We emphasize aspects such as the importance of using data augmentation, the need of separating the contribution of a classification network and the acquisition strategy to the overall performance, the advantages that a proper initialization of the network can bring to AL.
Learning Target Candidate Association to Keep Track of What Not to Track
To tackle the problem of lacking ground-truth correspondences between distractor objects in visual tracking, we propose a training strategy that combines partial annotations with self-supervision.
Ranked #2 on
Visual Object Tracking
on OTB-2015
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
In this paper, we analyze two popular network compression techniques, i. e. filter pruning and low-rank decomposition, in a unified sense.
Efficient Video Semantic Segmentation with Labels Propagation and Refinement
(ii) On the GPU, two Convolutional Neural Networks: A main segmentation network that is used to predict dense semantic labels from scratch, and a Refiner that is designed to improve predictions from previous frames with the help of a fast Inconsistencies Attention Module (IAM).
Adversarial Feature Distribution Alignment for Semi-Supervised Learning
We test our method on CIFAR10 and SVHN.
Adversarial Sampling for Active Learning
This paper proposes asal, a new GAN based active learning method that generates high entropy samples.
Towards Closing the Gap in Weakly Supervised Semantic Segmentation with DCNNs: Combining Local and Global Models
We reduce the gap by 64. 2% whereas the current state-of-the-art reduces it only by 57. 5%.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
