GLACIER is a single-stage transformer model treating MS/MS fragmentation as subgraph detection on molecular graphs, reporting 70.0% Top-1 accuracy on MassSpecGym and 8x speedup over prior two-stage methods.
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7 Pith papers cite this work. Polarity classification is still indexing.
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CAFE benchmark reveals that promptable segmentation models often produce correct masks for misleading prompts, showing a gap between localization accuracy and true concept understanding.
ReferEndoscopy plus attribute-retrieval and frequency-aware fusion yields open-vocabulary compositional referring segmentation that outperforms natural-image RIS baselines on endoscopic data and generalizes to an unseen robotic prostatectomy set.
Nonlinear Bipolar Compensation with Bipolar Logarithmic Transformation reduces outlier effects in post-training quantization by performing compensation in a compressed transformed space.
UniCon3R infers 4D contact from pose and geometry to correct human mesh and scene alignment in monocular video, yielding more physically plausible joint reconstructions than prior feed-forward methods.
Colinearity-Decay regularizer trains ViTs that maintain or improve full-precision accuracy while delivering higher accuracy after low-bit quantization on ImageNet and COCO tasks.
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
citing papers explorer
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GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem
GLACIER is a single-stage transformer model treating MS/MS fragmentation as subgraph detection on molecular graphs, reporting 70.0% Top-1 accuracy on MassSpecGym and 8x speedup over prior two-stage methods.
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From Pixels to Concepts: Do Segmentation Models Understand What They Segment?
CAFE benchmark reveals that promptable segmentation models often produce correct masks for misleading prompts, showing a gap between localization accuracy and true concept understanding.
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Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation
ReferEndoscopy plus attribute-retrieval and frequency-aware fusion yields open-vocabulary compositional referring segmentation that outperforms natural-image RIS baselines on endoscopic data and generalizes to an unseen robotic prostatectomy set.
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Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization
Nonlinear Bipolar Compensation with Bipolar Logarithmic Transformation reduces outlier effects in post-training quantization by performing compensation in a compressed transformed space.
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UniCon3R: Unified Contact-aware 4D Human-Scene Reconstruction from Monocular Video
UniCon3R infers 4D contact from pose and geometry to correct human mesh and scene alignment in monocular video, yielding more physically plausible joint reconstructions than prior feed-forward methods.
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Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
Colinearity-Decay regularizer trains ViTs that maintain or improve full-precision accuracy while delivering higher accuracy after low-bit quantization on ImageNet and COCO tasks.
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A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.