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The Visual Object Tracking VOT2016 Challenge Results
TLDR
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
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Hedged Deep Tracking
TLDR
A novel CNN based tracking framework is proposed, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one.
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CenterNet: Keypoint Triplets for Object Detection
TLDR
This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.
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Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
TLDR
A novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection and applies the proposed framework to optimize existing multi-level feature aggregation models and significantly improve their efficiency and accuracy.
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The Visual Object Tracking VOT2017 Challenge Results
The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art…
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The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
TLDR
This work constructs a new UAV benchmark focusing on complex scenarios with new level challenges, and is the first time to explore issues in unconstrained scenes comprehensively in UAV based real scenes.
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F3Net: Fusion, Feedback and Focus for Salient Object Detection
TLDR
The F3Net is proposed, which mainly consists of cross feature module (CFM) and cascaded feedback decoder (CFD) trained by minimizing a new pixel position aware loss (PPA), which aims to selectively aggregate multi-level features.
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Stacked Cross Refinement Network for Edge-Aware Salient Object Detection
TLDR
This framework aims to simultaneously refine multi-level features of salient object detection and edge detection by stacking Cross Refinement Unit (CRU), which outperforms existing state-of-the-art algorithms in both accuracy and efficiency.
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RAM: A Region-Aware Deep Model for Vehicle Re-Identification
TLDR
A novel learning algorithm is introduced to jointly use vehicle IDs, types/models, and colors to train the Region-Aware deep Model (RAM), which fuses more cues for training and results in more discriminative global and regional features.
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Measuring visual saliency by Site Entropy Rate
TLDR
A new visual saliency measure called Site Entropy Rate (SER) is proposed to compute the average information transmitted from a node to all the others during the random walk on the graphs/network to explain the center-surround mechanism from computation aspect.
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