S2M extracts structured text quadruples from change masks to provide noise-free multimodal supervision, achieving 17.80% Sek and 66.14% F_scd on the new Gaza-Change-v2 dataset and outperforming LLM-based multimodal methods.
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MaSC is a masked similarity metric that decomposes concept-driven image generation evaluation into subject-specific preservation and background-based prompt following using SigLIP2 embeddings, outperforming global baselines on human correlation and identity benchmarks.
Replacing softmax attention with entmax-1.5 in DINOv1 ViT-S/16 improves semantic segmentation mIoU on three benchmarks while keeping ImageNet linear-probing accuracy unchanged.
LingBot-VLA 2.0 combines 60k hours of multi-embodiment pretraining data, an expanded whole-body action space, and dual-query distillation from depth and video teachers to improve VLA performance on GM-100 and long-horizon mobile manipulation tasks.
citing papers explorer
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Masks Can Talk: Extracting Structured Text Information from Single-Modal Images for Remote Sensing Change Detection
S2M extracts structured text quadruples from change masks to provide noise-free multimodal supervision, achieving 17.80% Sek and 66.14% F_scd on the new Gaza-Change-v2 dataset and outperforming LLM-based multimodal methods.
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MaSC: A Masked Similarity Metric for Evaluating Concept-Driven Generation
MaSC is a masked similarity metric that decomposes concept-driven image generation evaluation into subject-specific preservation and background-based prompt following using SigLIP2 embeddings, outperforming global baselines on human correlation and identity benchmarks.
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Vision Transformers Need Better Token Interaction
Replacing softmax attention with entmax-1.5 in DINOv1 ViT-S/16 improves semantic segmentation mIoU on three benchmarks while keeping ImageNet linear-probing accuracy unchanged.
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From Foundation to Application: Improving VLA Models in Practice
LingBot-VLA 2.0 combines 60k hours of multi-embodiment pretraining data, an expanded whole-body action space, and dual-query distillation from depth and video teachers to improve VLA performance on GM-100 and long-horizon mobile manipulation tasks.
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