LangTail uses entity-level semantic priors from language models aligned via contrastive learning in a hierarchical clustering setup to resolve long-tail ambiguity, yielding +13.5, +12.9, and +8.9 mIoU gains on ScanNet-v2, S3DIS, and nuScenes.
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
13 Pith papers cite this work. Polarity classification is still indexing.
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VoxAfford fuses multi-scale voxel features into MLLM output tokens using cross-attention with a learned compatibility gate to achieve SOTA open-vocabulary 3D affordance detection with ~8% mIoU gain and zero-shot robot transfer.
This is the first comprehensive survey of LiDAR in rehabilitation, summarizing applications, AI techniques, trends, gaps, and future directions across studies from 2019-2025.
Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
HealthPoint represents clinical events as points in a 4D space (content, time, modality, case) and applies low-rank relational attention to achieve state-of-the-art mortality prediction from multi-level incomplete multimodal EHRs.
Proposes the first light field-LiDAR semantic segmentation dataset and the Mlpfseg network, which improves mIoU by 1.71 over image-only and 2.38 over point-cloud-only baselines via feature completion and depth perception modules.
PoHAR enables distributed air quality sensor networks to collaboratively detect hyperlocal indoor activities with 97.41% accuracy using conflict-free data replication, hierarchical clustering, and off-the-shelf ML classifiers on resource-constrained devices.
Fed3D is a federated 3D object detection system using local-global class-aware loss for heterogeneity and prompt modules for low-bandwidth communication, claiming better performance than prior methods on limited local data.
TAX-DPD combines a feed-forward dense GMM for global placement priors with disentangled point cloud diffusion for local geometry and pose to achieve precise robotic object placement.
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
PointCRA reduces information loss in deep point cloud networks by treating temporal trend variation as an extra evaluation dimension alongside spatial and channel attention, guided by a neighborhood homogeneity constraint.
A 360 RGB video pipeline using SfM, Grounded SAM, and RANSAC achieves 5-9% median relative DBH error, only 2-4% above LiDAR, on 61 acquisitions of 43 trees.