A rationale-informed pruning strategy for VLMs yields higher accuracy and more doubly-correct predictions than prior pruning methods on egocentric video benchmarks.
Precise benchmarking of explainable ai attribution methods,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Toward Low-Latency Vision-Language Models with Doubly-Correct Predictions in Egocentric Visual Understanding
A rationale-informed pruning strategy for VLMs yields higher accuracy and more doubly-correct predictions than prior pruning methods on egocentric video benchmarks.