Curating concise data for VLMs induces brevity, delivering 35x lower Cost-of-Pass at near-identical accuracy and higher matched-length accuracy than uncurated baselines.
Visual attention never fades: Selective progressive attention recalibration for detailed image captioning in multimodal large language models
7 Pith papers cite this work. Polarity classification is still indexing.
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TLVS mitigates hallucinations in LVLMs via token-level extraction and visual-sensitivity-adaptive steering applied only at critical decoding steps.
VLMs fail at exact visual path following primarily at self-intersections, where performance drops sharply after the first crossing on a controlled polyline benchmark.
Position and step penalty plus visual reasoning guidance fix premature answering and weak visual grounding in diffusion MLLMs, delivering up to 7.5% accuracy gains and over 3x speedup.
DRP decouples reasoning from perception in LMMs by using an LLM reasoner to query an LMM observer for visual details as needed, reducing visual grounding loss.
Proposes dynamic token re-weighting during target-domain fine-tuning to mitigate exacerbated attention sink in source-free CDFSL, achieving SOTA on four benchmarks.
SSA-ME uses saliency-aware modeling to reduce visual neglect and semantic drift, achieving SOTA results on the MMEB benchmark for multimodal retrieval.
citing papers explorer
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Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation
Curating concise data for VLMs induces brevity, delivering 35x lower Cost-of-Pass at near-identical accuracy and higher matched-length accuracy than uncurated baselines.
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Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation
TLVS mitigates hallucinations in LVLMs via token-level extraction and visual-sensitivity-adaptive steering applied only at critical decoding steps.
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TraversalBench: Challenging Paths to Follow for Vision Language Models
VLMs fail at exact visual path following primarily at self-intersections, where performance drops sharply after the first crossing on a controlled polyline benchmark.
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Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models
Position and step penalty plus visual reasoning guidance fix premature answering and weak visual grounding in diffusion MLLMs, delivering up to 7.5% accuracy gains and over 3x speedup.
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Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework
DRP decouples reasoning from perception in LMMs by using an LLM reasoner to query an LMM observer for visual details as needed, reducing visual grounding loss.
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Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning
Proposes dynamic token re-weighting during target-domain fine-tuning to mitigate exacerbated attention sink in source-free CDFSL, achieving SOTA on four benchmarks.
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Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval
SSA-ME uses saliency-aware modeling to reduce visual neglect and semantic drift, achieving SOTA results on the MMEB benchmark for multimodal retrieval.