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- BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation2,800
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.
- Cascaded Pyramid Network for Multi-person Pose Estimation1,795
A novel network structure called Cascaded Pyramid Network (CPN) is presented which targets to relieve the problem from these "hard" keypoints, with state-of-art results on the COCO keypoint benchmark, with average precision at 73.0.
- BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation1,770
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.
- Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network1,669
This work proposes a Global Convolutional Network to address both the classification and localization issues for the semantic segmentation and suggests a residual-based boundary refinement to further refine the object boundaries.
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- SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines982
This work proposes a set of practical guidelines of target state estimation for high-performance generic object tracker design and designs the Fully Convolutional Siamese tracker++ (SiamFC++), which achieves state-of-the-art performance on five challenging benchmarks, which proves both the tracking and generalization ability of the tracker.
- Learning a Discriminative Feature Network for Semantic Segmentation870
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.
- Shape Robust Text Detection With Progressive Scale Expansion Network860
A novel Progressive Scale Expansion Network (PSENet) is proposed, which can precisely detect text instances with arbitrary shapes and is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances.
- Executing your Commands via Motion Diffusion in Latent Space766
The proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages.
- Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network631
This paper proposes an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.
- AppAgent: Multimodal Agents as Smartphone Users555
A novel LLM-based multimodal agent framework designed to operate smartphone applications that allows the agent to mimic human-like interactions such as tapping and swiping through a simplified action space, eliminating the need for system back-end access and enhancing its versatility across various apps.
- High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification512
This paper proposes a novel framework by learning high-order relation and topology information for discriminative features and robust alignment for occluded person re-identification and significantly outperforms state-of-the-art mAP scores on Occluded-Duke dataset.
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- MegDet: A Large Mini-Batch Object Detector338
This paper proposes a Large Mini-Batch Object Detector (MegDet) to enable the training with a large minibatch size up to 256, so that it can effectively utilize at most 128 GPUs to significantly shorten the training time.
- TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation337
Experimental results demonstrate that the proposed TopFormer method significantly outperforms CNN- and ViT-based networks across several semantic segmentation datasets and achieves a good trade-off between accuracy and latency.
- DetNet: Design Backbone for Object Detection337
DetNet is proposed, which is a novel backbone network specifically designed for object detection that includes the extra stages against traditional backbone network for image classification, while maintains high spatial resolution in deeper layers.
- Context Prior for Scene Segmentation302
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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- 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.
- LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning290
LL3DA is presented, a Large Language 3D Assistant that takes point cloud as the direct input and responds to both text instructions and visual interactions that enables LMMs to better comprehend human interactions with the 3D environment and further remove the ambiguities within plain texts.
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- Scene Text Detection with Supervised Pyramid Context Network238
A supervised pyramid context network (SPCNET) is proposed to precisely locate text regions while suppressing false positives in scene text detection, based on Feature Pyramid Network (FPN) and instance segmentation.
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- Human pose estimation and its application to action recognition: A survey175
This survey focuses on 2D skeleton-based action recognition where the human poses are estimated from regular RGB images instead of depth images, and summarizes the performance of recent action recognition methods that use pose estimated from color images as input, showing that there is much room for improvements in this direction.
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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