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Works42 from public data
- SwinIR: Image Restoration Using Swin Transformer5,285
A strong baseline model SwinIR is proposed for image restoration based on the Swin Transformer that outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced byUp to 67%.
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- Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation359
This paper addresses the value of cross-image semantic relations for comprehensive object pattern mining and provides a unified framework that handles well different WSSS settings, i.e., learning WSSD with (1) precise image-level supervision only, (2) extra simple single-label data, and (3) extra noisy web data.
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- Fine-Grained Recognition: Accounting for Subtle Differences between Similar Classes147
The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other, and this work proposes to explicitly force the network to find the subtle differences among closely related classes.
- Object Counting and Instance Segmentation With Image-Level Supervision136
This work is the first to propose image-level supervised density map estimation for common object counting and demonstrate its effectiveness in image- level supervised instance segmentation and outperforms existing methods, including those using instance-level supervision, on both datasets for common objects counting.
- Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution120
Extensive experiments show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.
- Vision Transformers with Hierarchical Attention103
This paper proposes hierarchical MHSA (H-MHSA), a novel approach that computes sell-attention in a hierarchical fashion, and builds a family of hierarchical-attention-based transformer networks, namely HAT-Net, which provides a new perspective for vision transformers.
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- WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation53
This paper employs a random walk approach to dynamically draw positive samples from short random walk sequences, and a rank-aware negative sampling method to draw negative samples for efficiently learning the proposed pairwise ranking model.
- Rethinking Few-shot 3D Point Cloud Semantic Segmentation50
This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant is-sues in the state-of-the-art: foreground leakage and sparse point distribution, and proposes a novel FS-PCS model based on correlation optimization, referred to as Correlation Optimization Segmentation (COSeg).
- Learning Local and Global Temporal Contexts for Video Semantic Segmentation47
A Coarse-to-Fine Feature Mining (CFFM) technique to learn a unified presentation of LTC, and a CFFM++ by additionally learning GTC from the whole video to further exploit more temporal contexts.
- Object Segmentation by Mining Cross-Modal Semantics47
A novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy.
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- Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model32
This work introduces a GFS-PCS framework that synergizes dense but noisy pseudo-labels from 3D VLMs with precise yet sparse few-shot samples to maximize the strengths of both, named GFS-VL.
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- Multi-label Learning with Highly Incomplete Data via Collaborative Embedding29
Substantial experiments on public benchmark datasets and real security event data validate that the proposed weakly supervised multi-label learning approach can provide distinctively more accurate transductive and inductive classification than other state-of-the-art algorithms.
- Rethinking Global Context in Crowd Counting27
Extensive experiments on various datasets, including ShanghaiTech, UCF-QNRF, JHU-CROWD++ and NWPU, demonstrate that the proposed context extraction techniques can significantly improve the performance over the baselines.
- Edge Guided GANs With Multi-Scale Contrastive Learning for Semantic Image Synthesis25
A novel contrastive learning method, which aims to enforce pixel embeddings belonging to the same semantic class to generate more similar image content than those from different classes, and a novel multi-scale contrastiveLearning method that aims to push same-class features from different scales closer together to capture more semantic relations.
- EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmark21
EgoNight, the first comprehensive benchmark for nighttime egocentric vision, with visual question answering (VQA) as the core task, is presented, and EgoNight-VQA provides a strong foundation for advancing application-driven egocentric vision research and for developing models that generalize across illumination domains.
- When SAM2 meets video camouflaged object segmentation: a comprehensive evaluation and adaptation18
This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS) and demonstrates that SAM2 has the excellent zero-shot ability to detect camouflaged objects in videos.
- A Novel Framework for Node/Edge Attributed Graph Embedding18
This paper proposes GERI, which learns graph embedding with rich information in node/edge attributed graph through constructing a heterogeneous graph and upgrades GERI to semi-supervised GERI (named SGERI) by incorporating label information on nodes.
- Fixing Localization Errors to Improve Image Classification14
This work proposes a loss function that seeks to minimize peaks within the negative CAMs, called ‘ Homogeneous Negative CAM ’ loss, which provides an extra supervisory signal that helps the model to better discriminate between similar classes.
- Towards Open-Vocabulary Video Semantic Segmentation10
A robust baseline is proposed, OV2VSS, which integrates a spatial-temporal fusion module, allowing the model to utilize temporal relationships across consecutive frames, and a random frame enhancement module is incorporated, broadening the model's understanding of semantic context throughout the entire video sequence.
- Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation10
A novel solution THE-Mask for VSS is presented, which introduces temporal-aware hierarchical object queries for the first time and achieves state-of-the-art performance on the latest challenging VSS benchmark VSPW without bells and whistles.
- Using Machine Learning to Predict the In-Hospital Mortality in Women with ST-Segment Elevation Myocardial Infarction9
A machine-learning-based female-specific model can conveniently and effectively identify high-risk female STEMI patients who often suffer from an incorrect or delayed management.
- CompositeTasking: Understanding Images by Spatial Composition of Tasks7
A novel task conditioning model is introduced – a single encoder-decoder network that performs multiple, spatially varying tasks at once that not only offers a compact network for multitasking, but also allows for task-editing.
- Evaluating SAM2 for Video Semantic Segmentation5
This paper explores the extension of SAM2 for VSS, focusing on two primary approaches and highlighting firsthand observations and common challenges faced during this process, and suggests that leveraging SAM2 enhances overall performance in VSS, primarily due to its precise predictions of object boundaries.
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- A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects3
This survey reviews recent progress in VSP across five tasks, spanning Video Semantic Segmentation (VSS), Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), Video Tracking \&Segmentation (VTS), and Open-Vocabulary Video Segmentation (OVVS), and traces how each family models temporal context, preserves identity, and balances accuracy against efficiency.
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- Graph Embedding with Rich Information through Bipartite Heterogeneous Network.3
This paper proposed a novel and general framework of representation learning for graph with rich text information through constructing a bipartite heterogeneous network and designed a biased random walk to explore the constructed heterogeneity network with the notion of flexible neighborhood.
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Publication data from OpenAlex; 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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