Claims Browser

CODE framework with OWL-ViT L/14 backbone achieves 21.7 U-mAP and 40.8 K-mAP in Task 1 of Real-World Detection benchmark, surpassing previous state of the art by 2.6 and 2.3 points respectively

"with the OWL-ViT L/14 backbone, CODE achieves 21.7 U-mAP and 40.8 K-mAP in Task 1, surpassing the previous state of the art by 2.6 and 2.3 points, respectively"

Cross-Modal Joint Confidence Calibration using global visual prototypes can improve text-driven known-class predictions in object detection

"Cross-Modal Joint Confidence Calibration injects global visual prototypes to calibrate text-driven known-class predictions"

Dynamic margin-aware outlier suppression preserves ambiguous out-of-distribution instances better than rigid suppression methods

"Dynamic Outlier Suppression via Confidence Margin replaces rigid suppression with a margin-aware adjustment that preserves ambiguous out-of-distribution instances"

Current mental health assessment relies on episodic self-report scales that only provide sparse snapshots of wellbeing

"Mental health assessment relies on episodic self-report scales, which convert subjective states such as stress into numerical scores but provide only sparse snapshots of wellbeing."

Zero-shot agents rarely outperform a simple mean baseline, except with stronger backbones or compact, semantically meaningful features

"We find that zero-shot agents rarely outperform a simple mean baseline, except with stronger backbones or compact, semantically meaningful features."

Chain-of-thought prompting improves reasoning-oriented backbones, but does not guarantee temporal grounding or numerical correctness

"Chain-of-thought prompting improves reasoning-oriented backbones, but does not guarantee temporal grounding or numerical correctness."

State-aware Task Estimator and Planner (STEP) outperforms state-of-the-art by 32.8% in action executability and 14.8% in final-state error for robot assembly tasks

"Our approach outperforms the state-of-the-art by 32.8% in action executability and 14.8% in final-state error"

Prompting Multi-modal LLMs to explicitly estimate system state and predict state transitions ensures task-convergent planning in human-robot collaboration

"By forecasting future states alongside actions, STEP ensures task-convergent planning while also providing additional assistance parameters necessary for executing the predicted actions"

A self-supervised framework can learn joint visuospatial representations from RGB-D observations that integrates depth-derived geometric priors with visual backbones

"We introduce a self-supervised framework for learning joint visuospatial representations from RGB-D observations."

Modern vision foundation models are trained almost exclusively on RGB images despite many embodied systems having access to explicit depth sensing that provides geometric information monocular inputs cannot recover

"While modern vision foundation models are trained almost exclusively on RGB images, many embodied systems have access to explicit depth sensing, which provides geometric information that monocular inputs cannot recover."

The RGB-D representation method outperforms prior methods of comparable scale on multiple 3D geometry benchmarks

"it outperforms prior methods of comparable scale on multiple 3D geometry benchmarks"

The resulting representation shows promising improvements on 3D awareness while preserving semantic transfer and remains competitive on RGB-D semantic segmentation tasks

"The resulting representation shows promising improvements on 3D awareness while preserving semantic transfer: it outperforms prior methods of comparable scale on multiple 3D geometry benchmarks, and remains competitive when probed for standard RGB-D semantic segmentation tasks."

The field of Human Activity Recognition using IMUs still lacks a unified model that can generalize across diverse subjects, devices, and activities

"the field still lacks a unified model that can generalize across diverse subjects, devices, and activities"

HALO outperforms five state-of-the-art baselines on all 8 aggregate metrics when evaluated on 7 held-out datasets

"HALO outperforms five state-of-the-art baselines on all 8 aggregate metrics"

HALO achieves better performance than MOMENT foundation model despite using 10x fewer parameters (35M vs 341.2M)

"Despite using only ~35M trainable parameters -- 10x fewer than the latest foundation model MOMENT (341.2M) -- HALO improves zero-shot open-set accuracy"

HALO improves zero-shot open-set accuracy by 13.7 percentage points over MOMENT across 87 training labels

"HALO improves zero-shot open-set accuracy, measured over all 87 training labels, by 13.7 percentage points"

All models including HALO fail in zero-shot settings when faced with severe distribution shift on certain datasets

"On two further datasets with severe distribution shift, every model including HALO collapses zero-shot"

Language-aligned training with synonym-aware soft contrastive learning enables open-set recognition without per-dataset classifiers

"aligns this IMU encoder with text embeddings via synonym-aware soft contrastive learning, enabling open-set recognition via cosine-similarity retrieval without per-dataset classifiers"