Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks137 from public data
- TS2Vec: Towards Universal Representation of Time Series1,201
TS2Vec performs contrastive learning in a hierarchical way over augmented context views, which enables a robust contextual representation for each timestamp, which achieves significant improvement over existing SOTAs of unsupervised time series representation.
- Multivariate Time-Series Anomaly Detection via Graph Attention Network800
This paper proposes a novel self-supervised framework for multivariate time-series anomaly detection that outperforms other state-of-the-art models on three real-world datasets and has good interpretability and is useful for anomaly diagnosis.
- Semantic Flow for Fast and Accurate Scene Parsing456
This paper proposes a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels, and broadcast high-level features to high resolution features effectively and efficiently and exhibits superior performance over other real-time methods even on light-weight backbone networks.
- Improving Semantic Segmentation via Decoupled Body and Edge Supervision313
This paper proposes a new paradigm for semantic segmentation that establishes new state of the art while retaining high efficiency in inference and shows that the proposed framework with various baselines or backbone networks leads to better object inner consistency and object boundaries.
- Towards Open Vocabulary Learning: A Survey285
This paper thoroughly reviews open vocabulary learning, summarizing and analyzing recent developments in the field, and juxtaposing open vocabulary learning with analogous concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection.
- Gated Fully Fusion for Semantic Segmentation249
This paper proposes a new architecture, named Gated Fully Fusion(GFF), to selectively fuse features from multiple levels using gates in a fully connected way, and achieves the state of the art results on four challenging scene parsing datasets including Cityscapes, Pascal Context, COCO-stuff and ADE20K.
- TransVOD: End-to-End Video Object Detection With Spatial-Temporal Transformers242
TransVOD is presented, the first end-to-end video object detection system based on simple yet effective spatial-temporal Transformer architectures that streamline the pipeline of current VOD, effectively removing the need for many hand-crafted components for feature aggregation.
- Dual Graph Convolutional Network for Semantic Segmentation.196
The Dual Graph Convolutional Network (DGCNet) models the global context of the input feature by modelling two orthogonal graphs in a single framework, which achieves state-of-the-art results on both Cityscapes and Pascal Context datasets.
- Boundary Content Graph Neural Network for Temporal Action Proposal Generation185
A novel Boundary Content Graph Neural Network (BC-GNN) is proposed to model the insightful relations between the boundary and action content of temporal proposals by the graph neural networks.
- Sa2VA: Marrying SAM2 With MLLM for Dense Grounded Understanding of Images and Videos168
Experiments show that Sa2VA achieves strong performance across multiple tasks, particularly in referring video object segmentation, highlighting its potential for complex real-world applications.
- Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees135
This paper proposes a novel framework named Syntax-BERT, which works in a plug-and-play mode and is applicable to an arbitrary pre-trained checkpoint based on Transformer architecture and achieves consistent improvement over multiple pre- trained models, including BERT, RoBERTa, and T5.
- Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation125
Video K-Net is presented, a simple, strong, and unified framework for fully end-to-end video panoptic seg-mentation that achieves state-of-the-art videoPanoptic segmentation results on Citscapes-VPS and KITTI-STEP without bells and whistles and can serve as a new flexible baseline in video segmentation.
- Enhanced Boundary Learning for Glass-like Object Segmentation123
This paper proposes a novel refined differential module that outputs finer boundary cues and introduces an edge-aware point-based graph convolution network module to model the global shape along the boundary.
- End-to-End Video Object Detection with Spatial-Temporal Transformers116
TransVOD is presented, an end-to-end video object detection model based on a spatial-temporal Transformer architecture that streamline the pipeline of VOD, effectively removing the need for many hand-crafted components for feature aggregation, e.g., optical flow, recurrent neural networks, relation networks.
- 115
- PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation111
A point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow, is proposed, which reduces the noise introduced by the background while keeping efficiency.
- Tornado95
Empirical evaluation conducted on Tornado validates that various real-time iterative analysis tasks can improve their performance and efficiently tolerate failures with the proposed execution model.
- 83
- LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression81
A tailored solution named LadaBERT (Lightweight adaptation of BERT through hybrid model compression), which combines the advantages of different model compression methods, including weight pruning, matrix factorization and knowledge distillation, is proposed.
- Ranking Scientific Articles by Exploiting Citations, Authors, Journals, and Time Information78
This paper proposes a graph based ranking method, which utilizes citations, authors, journals/conferences and the publication time information collaboratively and shows that the approach is practical and ranks scientific articles more accurately than the state-of-art methods.
- Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow70
A Flow Alignment Module (FAM) is proposed to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high-resolution features effectively and efficiently and integrating this FAM to a standard feature pyramid structure exhibits superior performance over other real-time methods.
- TextNAS: A Neural Architecture Search Space Tailored for Text Representation67
Through automatic search, the discovered network architecture outperforms state-of-the-art models on various public datasets on text classification and natural language inference tasks and agrees well with human intuition.
- Towards Robust Referring Image Segmentation66
This work proposes a new formulation of RIS, named Robust Referring Image Segmentation (R-RIS), which considers the negative sentence inputs besides the regular positive text inputs, and proposes a new transformer-based model, called RefSegformer, with a token-based vision and language fusion module.
- A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network65
This paper introduces an innovative cooperative mechanism for state and reward design resulting in more effective and efficient transportation and shows that this approach can give rise to a significant improvement in terms of both performance and stability.
- Graph Pointer Neural Networks58
A pointer network is used to select the most relevant nodes from a large amount of multi-hop neighborhoods, which constructs an ordered sequence according to the relationship with the central node, and a pointer-network-based ranker in GPNN is joint-optimized with other parts in an end-to-end manner.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.