OBEYED-VLA improves VLA robustness in cluttered real-world manipulation by disentangling perception into VLM-based object-centric grounding and geometry-aware stages, then fine-tuning the policy only on single-object demonstrations.
Grad-cam: Visual explanations from deep networks via gradient-based localization
2 Pith papers cite this work. Polarity classification is still indexing.
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Integrated gradients on a 10-class domestic sound classifier yields 0.39 mean IoU, 0.52 frame F1 and 82.6% Pointing Game accuracy for temporal event detection, approaching weakly and strongly supervised framewise CNN baselines.
citing papers explorer
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Clutter-Robust Vision-Language-Action Models through Object-Centric and Geometry Grounding
OBEYED-VLA improves VLA robustness in cluttered real-world manipulation by disentangling perception into VLM-based object-centric grounding and geometry-aware stages, then fine-tuning the policy only on single-object demonstrations.
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Evaluating the Temporal Detection Capability of Integrated Gradients Applied on Sound Classifier
Integrated gradients on a 10-class domestic sound classifier yields 0.39 mean IoU, 0.52 frame F1 and 82.6% Pointing Game accuracy for temporal event detection, approaching weakly and strongly supervised framewise CNN baselines.