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- BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation2,851
A novel Bilateral Segmentation Network (BiSeNet) is proposed that makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets.
- BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation1,760
This work proposes an efficient and effective architecture with a good trade-off between speed and accuracy, termed Bilateral Segmentation Network (BiSeNet V2), and designs a guided aggregation layer to enhance mutual connections and fuse both types of feature representation.
- Learning a Discriminative Feature Network for Semantic Segmentation871
This work proposes a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network, which is specially design to handle the intra-class inconsistency problem and to make the bilateral features of boundary distinguishable with deep semantic boundary supervision.
- Lite-HRNet: A Lightweight High-Resolution Network503
An efficient high-resolution network, Lite-HRNet, is presented, which demonstrates superior results on human pose estimation over popular lightweight networks and can be easily applied to semantic segmentation task in the same lightweight manner.
- Domain Adaptation for Image Dehazing477
This work proposes a domain adaptation paradigm, which consists of an image translation module and two image dehazing modules, and applies a bidirectional translation network to bridge the gap between the synthetic and real domains by translating images from one domain to another.
- Context Prior for Scene Segmentation313
This work develops a Context Prior with the supervision of the Affinity Loss, an effective Context Prior Network that can selectively capture the intra-class and inter-class contextual dependencies, leading to robust feature representation.
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- Temporal Context Aggregation Network for Temporal Action Proposal Refinement224
A Local-Global Temporal Encoder (LGTE) and Temporal Boundary Regressor (TBR) are designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement.
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- OadTR: Online Action Detection with Transformers174
The experimental results show that OadTR achieves higher training and inference speeds than current RNN based approaches, and significantly outperforms the state-of-the-art methods in terms of both mAP and mcAP.
- Re-ID Driven Localization Refinement for Person Search160
A differentiable ROI transform layer is developed to effectively transform the bounding boxes from the original images so that the box coordinates can be supervised by the re-ID training other than the original detection task.
- Learning a Discriminative Prior for Blind Image Deblurring160
This work forms the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN) and is able to distinguish whether an input image is clear or not.
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- Hybrid Relation Guided Set Matching for Few-shot Action Recognition138
A novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric that can be highly informative and flexible to predict query categories under the few-shot settings is proposed.
- UniAnimate: taming unified video diffusion models for consistent human image animation124
This work proposes an alternative temporal modeling architecture based on a state space model to replace the original computation-consuming temporal Transformer and achieves superior synthesis results over existing state-of-the-art counterparts in both quantitative and qualitative evaluations.
- Blind Image Deblurring via Deep Discriminative Priors118
This work forms the image prior as a binary classifier using a deep convolutional neural network to handle image dehazing and develops an efficient numerical approach based on the half-quadratic splitting method and gradient descent algorithm to optimize the proposed model.
- CLIP-guided Prototype Modulating for Few-shot Action Recognition106
This work presents a CLIP-guided prototype modulating framework called CLIP-FSAR, which consists of two key components: a video-text contrastive objective and a prototype modulation that bridges the task discrepancy between CLIP and the few-shot video task by contrasting videos and corresponding class text descriptions.
- VideoLCM: Video Latent Consistency Model100
The VideoLCM framework is presented, which leverages the concept of consistency models from image generation to efficiently synthesize videos with minimal steps while maintaining high quality and computational efficiency.
- MoLo: Motion-Augmented Long-Short Contrastive Learning for Few-Shot Action Recognition100
A Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder, that can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching.
- PLIP: Language-Image Pre-training for Person Representation Learning96
A novel language-image pre-training framework for person representation learning, termed PLIP, which significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings.
- Structure-aware human pose estimation with graph convolutional networks94
This paper proposes a novel model, termed Pose Graph Convolutional Network (PGCN), to exploit the structural relationships between body key points to exploit these important relationships for pose estimation, and shows that this method consistently outperforms competing state-of-the-art methods.
- Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity91
HIVAU-70k is introduced, a large-scale benchmark for hierarchical video anomaly understanding across any granularity and a semi-automated annotation engine that efficiently scales high-quality annotations by combining manual video segmentation with recursive free-text annotation using large language models (LLMs).
- Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM89
Extensive experimental results validate the generality and interpretability of the proposed Holmes-VAD, establishing it as a novel interpretable technique for real-world video anomaly analysis.
- FGN: Fully Guided Network for Few-Shot Instance Segmentation84
A Fully Guided Network for few-shot instance segmentation is presented, which introduces different guidance mechanisms into the various key components in Mask R-CNN in order to make full use of the guidance effect from the support set and adapt better to the inter-class generalization.
- UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity80
CFAM, a more efficient al-gorithm especially designed for text-based person retrieval with ultra fine-grained texts is proposed, which achieves fine granularity mining by adopting a shared cross-modal granularity de-coder and hard negative match mechanism.
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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