ACG mitigates hallucinations in LVLMs via single-pass contrastive guidance in attention space that suppresses text-only biases through masking and orthogonal projection.
Instructblip: Towards general- purpose vision-language models with instruction tuning
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.CV 8roles
background 2polarities
background 2representative citing papers
DualToken disentangles semantics and appearance via separate codebooks in one tokenizer, reporting 0.25 rFID, 82% ImageNet zero-shot accuracy, and gains over VILA-U on understanding and generation benchmarks.
SENTINEL reduces MLLM object hallucinations by over 90% via sentence-level early intervention with detector-bootstrapped preference data and C-DPO loss, outperforming prior SOTA on hallucination and capability benchmarks.
A new 1.2M-caption dataset generated via GPT-4V improves LMMs on MME and MMBench by 222.8/22.0/22.3 and 2.7/1.3/1.5 points respectively when used for supervised fine-tuning.
TemporalVLM adds timestamp-aware clip encoding and BiLSTM global aggregation to video LLMs, introduces the IndustryASM factory dataset, and reports outperformance on dense captioning, temporal grounding, highlight detection, and action segmentation.
InternLM-XComposer-2.5 is a 7B vision-language model supporting up to 96K context that reaches GPT-4V-level performance on image, video, and multi-turn tasks and adds LoRA-driven text-image composition capabilities.
InternLM-XComposer2 introduces Partial LoRA on InternLM2-7B to enable high-quality free-form text-image composition while matching or exceeding GPT-4V on select vision-language benchmarks.
A training-free framework generates expressive, character-grounded dialogue and speech from scene prompts using vision-language encoders, LLMs, and a recursive narrative memory bank for cross-scene consistency.
citing papers explorer
-
Attention-space Contrastive Guidance for Efficient Hallucination Mitigation in LVLMs
ACG mitigates hallucinations in LVLMs via single-pass contrastive guidance in attention space that suppresses text-only biases through masking and orthogonal projection.
-
DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies
DualToken disentangles semantics and appearance via separate codebooks in one tokenizer, reporting 0.25 rFID, 82% ImageNet zero-shot accuracy, and gains over VILA-U on understanding and generation benchmarks.
-
Mitigating Object Hallucinations via Sentence-Level Early Intervention
SENTINEL reduces MLLM object hallucinations by over 90% via sentence-level early intervention with detector-bootstrapped preference data and C-DPO loss, outperforming prior SOTA on hallucination and capability benchmarks.
-
ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
A new 1.2M-caption dataset generated via GPT-4V improves LMMs on MME and MMBench by 222.8/22.0/22.3 and 2.7/1.3/1.5 points respectively when used for supervised fine-tuning.
-
TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos
TemporalVLM adds timestamp-aware clip encoding and BiLSTM global aggregation to video LLMs, introduces the IndustryASM factory dataset, and reports outperformance on dense captioning, temporal grounding, highlight detection, and action segmentation.
-
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
InternLM-XComposer-2.5 is a 7B vision-language model supporting up to 96K context that reaches GPT-4V-level performance on image, video, and multi-turn tasks and adds LoRA-driven text-image composition capabilities.
-
InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model
InternLM-XComposer2 introduces Partial LoRA on InternLM2-7B to enable high-quality free-form text-image composition while matching or exceeding GPT-4V on select vision-language benchmarks.
-
Character-Centered Dialogue Generation from Scene-Level Prompts
A training-free framework generates expressive, character-grounded dialogue and speech from scene prompts using vision-language encoders, LLMs, and a recursive narrative memory bank for cross-scene consistency.