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- 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.
- The Visual Object Tracking VOT2016 Challenge Results776
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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- RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment558
A novel and end-to-end Alignment Generative Adversarial Network (AlignGAN) for the RGB-IR RE-ID task, which consists of a pixel generator, a feature generator and a joint discriminator that is able to not only alleviate the cross-modality and intra- modality variations, but also learn identity-consistent features.
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- Robust Visual Tracking via Structured Multi-Task Sparse Learning427
The results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity.
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- Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer419
This work designs two effective mechanisms including part diversity and part discriminability that can achieve diverse part discovery for occluded person Re-ID in a weakly supervised manner and performs favor-ably against stat-of-the-art methods.
- Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer417
This paper proposes a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and themodality-specific characteristics to boost the reidentification performance.
- Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification381
This paper proposes to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments for RGB-IR Re-ID and demonstrates that the proposed model favourably against state-of-the-art methods.
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- Geometry Uncertainty Projection Network for Monocular 3D Object Detection316
A GUP Net is proposed to tackle the error amplification problem at both inference and training stages and can infer more reliable object depth than existing methods and outperforms the state-of-the-art image-based monocular 3D detectors.
- Graph Structured Network for Image-Text Matching311
This paper presents a novel Graph Structured Matching Network (GSMN) to learn fine-grained correspondence of structured phrase correspondence, and shows that GSMN outperforms state-of-the-art methods on benchmarks.
- Hi, magic closet, tell me what to wear!290
This paper collects a large clothing What-to-Wear dataset, and thoroughly annotates the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk, to learn a generalize-well model and comprehensively evaluate it.
- Robust Visual Tracking Via Consistent Low-Rank Sparse Learning261
A consistent low-rank sparse tracker that builds upon the particle filter framework for tracking that adaptively prunes and selects candidate particles by using linear sparse combinations of dictionary templates is proposed.
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- Low-Rank Sparse Learning for Robust Visual Tracking254
By representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles.
- I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs229
This work proposes an end-to-end ZSAR framework based on a structured knowledge graph, which can jointly model the relationships between action-attribute, action-action, and attribute-attribute and designs a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch.
- Focus Your Attention213
A novel Bidirectional Focal Attention Network (BFAN) is presented, which not only allows to attend to relevant fragments but also diverts all the attention into these relevant fragments to concentrate on them, which enables to avoid preference to long text or complex image.
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- A Keypoint-based Global Association Network for Lane Detection191
A Global Association Network (GANet) is proposed to formulate the lane detection problem from a new perspective, where each keypoint is directly regressed to the starting point of the lane line instead of point-by-point extension, which outperforms previous methods.
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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 2026-10-13. 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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