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- DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting127
DeepSolo, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously, and introduces a text-matching criterion to deliver more accurate supervisory signals, thus enabling more efficient training.
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- UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather53
The proposed UniMix efficiently mitigates the domain gap and enables LSS models to learn weather-robust and domain-invariant representations and achieves superior performance over state-of-the-art methods.
- SimDistill: Simulated Multi-Modal Distillation for BEV 3D Object Detection35
A Simulated multi-modal Distillation (SimDistill) method that supports intra-modal, cross-modal, and multi-modal fusion distillation simultaneously in the Bird's-eye-view space and can learn better feature representations for 3D object detection while maintaining a cost-effective camera-only deployment.
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- In Situ Measurement of Deep-Sea Salinity Using Optical Salinometer Based on Michelson Interferometer10
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- Omniforce: on human-centered, large model empowered and cloud-edge collaborative AutoML system9
OmniForce is introduced, a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques, putting an AutoML system into practice and building AI in open-environment scenarios, such as using large models in industrial supply chains.
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- Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning3
Comprehensive experiments on downstream point cloud tasks and ablation studies demonstrate that the SD model can enhance point cloud self-supervised learning.
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- Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment2
This work proposes a novel learning strategy to regularize the pseudo-labels generated for training, thus effectively narrowing the gaps between pseudo-labels and model predictions and introduces an Entropy Regularization loss and a Distribution Alignment loss for label-efficient learning, resulting in an ERDA learning strategy.
- A 3D open-framework amino acid templated cerium phosphite-oxalate showing proton conductive property2
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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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