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- LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking1,655
LaSOT is presented, a high-quality benchmark for Large-scale Single Object Tracking that consists of 1,400 sequences with more than 3.5M frames in total, and is the largest, to the best of the authors' knowledge, densely annotated tracking benchmark.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
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- Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking458
A multi-stage tracking framework, Siamese Cascaded RPN (C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in aSiamese network, which achieves state-of-the-art results and runs in real-time.
- Clustered Object Detection in Aerial Images437
This paper proposes a Clustered Detection (ClusDet) network that unifies object clustering and detection in an end-to-end framework and achieves promising performance in comparison with state-of-the-art detectors.
- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
- Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking329
In the extensive experiments on popular benchmarks including OTB2013, OTB2015, TC128 and UAV20L, PTAV achieves the best tracking accuracy among all real-time trackers, and in fact performs even better than many deep learning based solutions.
- VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results259
Based on the selected 29 robust detection methods, the experimental results comprehensively are discussed, which shows the effectiveness of ensemble learning and data augmentation in drone captured object detection.
- SANet: Structure-Aware Network for Visual Tracking256
This work utilizes recurrent neural network (RNN) to model object structure, and incorporates it into CNN to improve its robustness to similar distractors and shows that the proposed algorithm outperforms other methods.
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- Online Multi-Object Tracking With Instance-Aware Tracker and Dynamic Model Refreshment125
This paper proposes an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models, and considers response maps from all target models and assigns spatial locations exclusively to optimize the overall accuracy.
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- Learning Target-aware Representation for Visual Tracking via Informative Interactions83
A novel backbone architecture with multiple branch-wise interactions inside the Siamese-like backbone networks (InBN) that injects the target information to different stages of the backbone network, leading to better target-perception of candidate feature representation with negligible computation cost.
- AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild76
This work introduces AnimalTrack, a dedicated benchmark for multi-animal tracking in the wild and to understand how existing MOT algorithms perform on AnimalTrack and provide baselines for future comparison, extensively evaluate 14 state-of-the-art representative trackers.
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- Context-Guided Spatio-Temporal Video Grounding65
A novel framework, context-guided STVG (CG-STVG), which mines discriminative instance context for object in videos and applies it as a supplementary guidance for target localization for more accurate target localization.
- Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning64
A surprisingly simple yet effective dual-prompt learning network (dubbed DPLNet) for training-efficient multimodal semantic segmentation, which achieves new state-of-the-art performance or is on a par with other complex approaches on four RGB-D/T semantic segmentation datasets while satisfying parameter efficiency.
- Accurate and Fast Compressed Video Captioning63
A simple yet effective end-to-end transformer in the compressed domain for video captioning that enables learning from the compressed video for captioning and can achieve state-of-the-art performance on different benchmarks while running almost 2× faster than existing approaches.
- PIDray: A Large-Scale X-ray Benchmark for Real-World Prohibited Item Detection61
A tree-like structure is adopted to suppress the influence of the long-tailed issue in the PIDray dataset, where the first course-grained node is tasked with the binary classification to alleviate the influence of head category, while the subsequent fine-grained node is dedicated to the specific tasks of the tail categories.
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- Robust Domain Adaptive Object Detection With Unified Multi-Granularity Alignment38
A unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning and an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness.
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- Transparent Object Tracking Benchmark34
A novel tracker is introduced, named TransATOM, which leverages transparency features for tracking and surpasses all 25 evaluated approaches by a large margin and is expected to facilitate future research and application of transparent object tracking in both the academia and industry.
- Neural Radiance Fields in Medical Imaging: A Survey29
This paper presents a comprehensive examination of applications of NeRFs in medical imaging, highlighting four imminent challenges, including fundamental imaging principles, inner structure requirement, object boundary definition, and color density significance.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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