DO-Bench is a controlled benchmark that attributes VLM object hallucination errors to textual prior pressure, perceptual limits, or their interaction via two diagnostic dimensions and metrics.
In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition
3 Pith papers cite this work. Polarity classification is still indexing.
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HyLaR interleaves discrete text generation with continuous visual latent representations and optimizes them via a decoupled RL algorithm using vMF distributions, improving fine-grained visual reasoning.
SMART expands speculative decoding trees only when a node's marginal benefit-cost ratio exceeds current tree-level speedup, claiming ~15–20% extra wall-clock speedup without quality loss.
citing papers explorer
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DO-Bench: An Attributable Benchmark for Diagnosing Object Hallucination in Vision-Language Models
DO-Bench is a controlled benchmark that attributes VLM object hallucination errors to textual prior pressure, perceptual limits, or their interaction via two diagnostic dimensions and metrics.
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HyLaR: Hybrid Latent Reasoning with Decoupled Policy Optimization
HyLaR interleaves discrete text generation with continuous visual latent representations and optimizes them via a decoupled RL algorithm using vMF distributions, improving fine-grained visual reasoning.
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SMART: When is it Actually Worth Expanding a Speculative Tree?
SMART expands speculative decoding trees only when a node's marginal benefit-cost ratio exceeds current tree-level speedup, claiming ~15–20% extra wall-clock speedup without quality loss.