Presents the first large-scale infrared off-road dataset and a flow-free temporal model achieving state-of-the-art freespace detection performance with real-time inference.
Segformer: Simple and efficient design for semantic segmentation with transformers,
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
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.
WILD-SAM is a fine-tuned SAM variant using phase-aware MoE adapters and wavelet subband enhancement that achieves state-of-the-art landslide detection on wrapped InSAR data.
Knowledge distillation plus quantization on U-Net variants, combined with custom FPGA hardware, yields 398 FPS at 204.99 Frames/J and raises mean IoU to 71.92% on CrackVision12K, an 8.82 pps gain over prior results.
citing papers explorer
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Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark
Presents the first large-scale infrared off-road dataset and a flow-free temporal model achieving state-of-the-art freespace detection performance with real-time inference.
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Geometry-Aware Cross Modal Alignment for Light Field-LiDAR Semantic Segmentation
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.
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WILD-SAM: Phase-Aware Expert Adaptation of SAM for Landslide Detection in Wrapped InSAR Interferograms
WILD-SAM is a fine-tuned SAM variant using phase-aware MoE adapters and wavelet subband enhancement that achieves state-of-the-art landslide detection on wrapped InSAR data.
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A Case Study on Energy-Efficient Edge AI Crack Segmentation
Knowledge distillation plus quantization on U-Net variants, combined with custom FPGA hardware, yields 398 FPS at 204.99 Frames/J and raises mean IoU to 71.92% on CrackVision12K, an 8.82 pps gain over prior results.