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- Geometric Transformer for Fast and Robust Point Cloud Registration514
This work proposes Geometric Transformer, a simplistic design that attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to 100 times acceleration.
- GeoTransformer: Fast and Robust Point Cloud Registration With Geometric Transformer303
Geometric Transformer is proposed, or GeoTransformer for short, to learn geometric feature for robust superpoint matching, making it invariant to rigid transformation and robust in low-overlap cases.
- ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices292
benefit from the highly efficient backbone and detection part design, ThunderNet surpasses previous lightweight one-stage detectors with only 40% of the computational cost on PASCAL VOC and COCO benchmarks.
- Similarity measures between intuitionistic fuzzy (vague) sets: A comparative analysis279
The research presented here could benefit selection and applications of similarity measures for intuitionistic fuzzy sets and vague sets in practice.
- Rotation-Invariant Transformer for Point Cloud Matching179
This work introduces RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task, and proposes a global transformer with rotation-invariant cross-frame spatial awareness learned by the self-attention mechanism, which significantly improves the feature distinctiveness and makes the model robust with respect to the low overlap.
- MotionTrack: Learning Robust Short-Term and Long-Term Motions for Multi-Object Tracking172
A simple yet effective multi-object tracker, i.e., MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range, is proposed.
- Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence140
The proposed ThreatRaptor is an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text and provides a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities.
- Fd-Mobilenet: Improved Mobilenet with a Fast Downsampling Strategy113
Fast-Downsampling MobileNet is presented, an efficient and accurate network for very limited computational budgets (e.g., 10–140 MFLOPs) that consistently outperforms MobileNet and achieves comparable results with ShufflieNet under different computational budgets.
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- RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration88
RIGA is introduced to learn descriptors that are Rotation-Invariant by design and Globally-Aware to learn descriptors that are Rotation-Invariant by design and Globally-Aware for point cloud registration.
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- SC2Net: Sparse LSTMs for Sparse Coding61
This work recasts ISTA as a recurrent neural network unit and shows its connection with the well-known long short term memory (LSTM) model, and presents a neural network to achieve sparse codes in an end-to-end manner.
- Towards Generalizable Multi-Object Tracking58
This paper investigates the factors that influence trackers' generalization to different scenar-ios and concretize them into a set of tracking scenario at-tributes to guide the design of more generalizable trackers and proposes a "point-wise to instance-wise relation" framework for MOT, i.e., GeneralTrack, which can generalize across diverse scenarios while eliminating the need to balance motion and appearance.
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- Task-Specific Context Decoupling for Object Detection57
This work proposes a novel Task-Specific COntext DEcoupling (TSCODE) head which further disentangles the feature encoding for two tasks, and provides high-resolution feature map containing more edge information to better regress object boundaries.
- Multi-Prompts Learning with Cross-Modal Alignment for Attribute-based Person Re-Identification53
A new framework called Multi-Prompts ReID (MP-ReID), based on prompt learning and language models, to fully dip fine attributes to assist ReID task with off-the-shelf models for more accurate retrieval results.
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- 2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds43
2D3D-MATR, a detection-free method for accurate and robust registration between images and point clouds that adopts a coarse-to-fine pipeline where it first computes coarse correspondences between downsampled patches of the input image and the point cloud and then extends them to form dense correspondences between pixels and points within the patch region.
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- Real-time texture-mapped vector glyphs42
A vector graphics representation suitable for real-time rendering on GPUs that supports inexpensive high-quality anisotropic antialiasing as well as special effects such as outlining (with both rounded and sharp miters) and embossing.
- Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration41
Graph-based Spatial Consistency Network (GraphSCNet) is proposed to filter outliers for non-rigid registration and effectively improves the quality of the putative correspondences and attains state-of-the-art performance on three challenging benchmarks.
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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 2026-10-12. 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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