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From the 4 of 121 papers with an AI index.

most citedMSNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

80 citations

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20 papers · 1 filter

cs.CV2026

Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration

Aoru Xue, Yujing Sun, Yiming Ren +3

We propose Sen-Cap, a Sensor-Flexible and Noise-Resilient 3D human motion Capture framework that integrates multi-modal data from LiDAR and camera. While multi-modal sensors provid…

cs.CV20262 cited

Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning

Hao Kong, Di Liu, Xiangzhong Luo +5

The paper introduces TECO, a framework that jointly prunes depth, width, and input resolution of convolutional neural networks to improve speed and resource usage on embedded devic…

cs.CV20262 cited

FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection

Vikash Sathiamoorthy, Shuo Huai, Hao Kong +7

Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with o…

cs.CV2026

CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training

Zeru Yang, Fang-Ying Gong, Steve H. L. Yim +1

Accurately estimating urban carbon emissions is critical for sustainable urban planning, yet many existing approaches remain difficult to apply consistently across cities due to da…

cs.CV20266 cited

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

Hao Kong, Di Liu, Shuo Huai +5

Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment…

cs.CV20262 cited

Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardware

Hao Kong, Di Liu, Shuo Huai +5

Scaling down the resolution of input images can greatly reduce the computational overhead of convolutional neural networks (CNNs), which is promising for edge AI. However, as an im…