Dual-level semantic-geometric dynamic probabilities let monocular Gaussian SLAM use transiently static objects for tracking yet prune them from the map, cutting ATE up to 13% and removing artifacts.
arXiv preprint arXiv:2312.09579 (2023)
6 Pith papers cite this work. Polarity classification is still indexing.
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SegFS is a dual-path architecture that uses sparse keyframe open-vocabulary predictions to condition a fast feature-space network for efficient temporal instance segmentation in videos.
RS4D distills ViT knowledge into SSM backbones for remote sensing instance segmentation, delivering 8x fewer parameters and 9x fewer FLOPs than ViT methods while matching or exceeding accuracy on SSDD, WHU, and NWPU datasets.
PhysGraph reconstructs object-centric 3D geometry from RGB-D, decomposes objects into parts, infers materials and articulations via visual reasoning, and reports SOTA results on semantic segmentation, multi-object mass estimation, and articulation prediction.
Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical foundation models.
citing papers explorer
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DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability
Dual-level semantic-geometric dynamic probabilities let monocular Gaussian SLAM use transiently static objects for tracking yet prune them from the map, cutting ATE up to 13% and removing artifacts.
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Segmenting, Fast and Slow: Real-Time Open-Vocabulary Video Instance Segmentation with Dual-Path Processing
SegFS is a dual-path architecture that uses sparse keyframe open-vocabulary predictions to condition a fast feature-space network for efficient temporal instance segmentation in videos.
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Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models
RS4D distills ViT knowledge into SSM backbones for remote sensing instance segmentation, delivering 8x fewer parameters and 9x fewer FLOPs than ViT methods while matching or exceeding accuracy on SSDD, WHU, and NWPU datasets.
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PhysGraph: A Physics-aware 3D Scene Graph for Perception and Reasoning
PhysGraph reconstructs object-centric 3D geometry from RGB-D, decomposes objects into parts, infers materials and articulations via visual reasoning, and reports SOTA results on semantic segmentation, multi-object mass estimation, and articulation prediction.
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Weight Group-wise Post-Training Quantization for Medical Foundation Model
Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical foundation models.
- On Efficient Variants of Segment Anything Model: A Survey