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- The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking1,076
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.
- Siamese Box Adaptive Network for Visual Tracking993
SiamBAN views the visual tracking problem as a parallel classification and regression problem, and thus directly classifies objects and regresses their bounding boxes in a unified FCN, making SiamB Ban more flexible and general.
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- The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline247
A novel approach based on the so-called Context-aware Multi-task Siamese Network (CMSN) model that explores new cues in UAV videos by judging the consistency degree between objects and contexts and that can be used for SOT and MOT is proposed.
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- Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection143
This work designs a multi-head classification module (each head serves as a classifier) with a diversity loss to maximize the distribution differences of predicted pseudo labels across heads and devise an iterative uncertainty pseudo label refinement strategy, which improves not only the initial pseudo labels but also the updated ones obtained by the desired classifier in the second stage.
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- Toward Unified Token Learning for Vision-Language Tracking136
A simple, flexible and effective vision-language (VL) tracking pipeline, termed MMTrack, which casts VL tracking as a token generation task, and the design without other prior modules avoids multiple sub-tasks learning and hand-designed loss functions, significantly reducing the complexity of VL tracking modeling.
- Learning to Filter: Siamese Relation Network for Robust Tracking134
A novel Siamese relation network is proposed, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM), which performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into theSiamese framework to generate accurate tracking result.
- Hierarchical Modular Network for Video Captioning111
A hierarchical modular network to bridge video representations and linguistic semantics from three levels before generating captions is proposed and shows that the proposed method performs favorably against the state-of-the-art models on the two widely-used benchmarks.
- Multi-label classification by exploiting local positive and negative pairwise label correlation105
A simple and effective Bayesian model for multi-label classification by exploiting Local positive and negative Pairwise Label Correlations, named LPLC is proposed.
- Weakly-Supervised Crowd Counting Learns from Sorting Rather Than Locations98
A weakly-supervised counting network is proposed, which directly regresses the crowd numbers without the location supervision, and a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers.
- Spatiotemporal CNN for Video Object Segmentation98
A unified, end-to-end trainable spatiotemporal CNN model for VOS, which consists of two branches, i.e., the temporal coherence branch and the spatial segmentation branch, which is designed to capture the dynamic appearance and motion cues of video sequences to guide object segmentation.
- Real-time Visual Object Tracking with Natural Language Description86
This work argues that conditioning on the natural language (NL) description of a target provides information for longer-term invariance, and thus helps cope with typical tracking challenges, and proposes a novel deep tracking-by-detection formulation that can take advantage of NL descriptions.
- VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results81
A large-scale video object detection and tracking dataset, which consists of 79 video clips with about 1.5 million annotated bounding boxes in 33, 366 frames, and the evaluation protocol of the VisDrone-VDT2018 challenge and the results of the algorithms on the benchmark dataset are presented.
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- Multi-Attention Network for Compressed Video Referring Object Segmentation69
A multi-attention network which consists of dual-path dual-att attention module and a query-based cross-modal Transformer module designed to extract effective representation from compressed data in three modalities, i.e., I-frame, Motion Vector and Residual is proposed.
- Rethinking Sampling Strategies for Unsupervised Person Re-Identification67
It is suggested that deteriorated over-fitting is an important factor causing poor performance, and enhancing statistical stability can rectify this problem, and a simple yet effective approach is proposed, termed group sampling, which gathers samples from the same class into groups.
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- Towards Universal Modal Tracking With Online Dense Temporal Token Learning63
This work proposes a universal video-level modality-awareness tracking model with online dense temporal token learning (called UM-ODTrack), designed to support various tracking tasks, utilizing the same model architecture and parameters, and introduces two simple yet effective online dense temporal token association mechanisms.
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 2026-10-15. 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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