| Beyond Language Priors: Diagnosing and Fixing Visual-Origin Hallucinations in Multimodal LLM added by Syntology |
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zxp555/ACFT_MM26/ACFT/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models added by Syntology |
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Zhong-Chenchen/DIVE/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models added by Syntology |
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rapidfuzz/RapidFuzz/src/rapidfuzz/_utils.py eabd22370da53c9e |
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MIT (permissive) |
| MLLM-DataEngine: Closing the Loop of Multimodal Instruction Tuning Data Generation added by Syntology |
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opendatalab/MLLM-DataEngine/LLaVA/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Uncertainty-Aware Knowledge Distillation for Multimodal Large Language Models added by Syntology |
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| HiDrop: Hierarchical Vision Token Reduction in MLLMs via Late Injection, Concave Pyramid Pruning, and Early Exit added by Syntology |
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EIT-NLP/HiDrop/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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Apache-2.0 (permissive) |
| Beyond Next-Token Alignment: Distilling Multimodal Large Language Models via Token Interactions added by Syntology |
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| LLaVA-SP: Enhancing Visual Representation with Visual Spatial Tokens for MLLMs |
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CnFaker/LLaVA-SP/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs |
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theia-4869/cdpruner/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation |
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| arXiv:2504.00502 |
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icip-cas/ShortV/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-tuning |
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| Stop Looking for Important Tokens in Multimodal Language Models: Duplication Matters More |
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| Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models |
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| EVEv2: Improved Baselines for Encoder-Free Vision-Language Models |
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| [CLS] Token Tells Everything Needed for Training-free Efficient MLLMs |
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thu-mig/vtc-cls/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft |
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| p-MoD: Building Mixture-of-Depths MLLMs via Progressive Ratio Decay |
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| FlashSloth: Lightning Multimodal Large Language Models via Embedded Visual Compression |
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| Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs |
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theia-4869/fastervlm/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering |
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aimagelab/reflectiva/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks |
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ASTRAL-Group/ASTRA/utility_eval/minigpt_mmbench.py bae18947b56f2be1 |
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| Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model |
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liuting20/mustdrop/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization |
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yuxixie/v-dpo/llava_dpo/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction |
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cooperx521/pyramiddrop/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| LLaVA-KD: A Framework of Distilling Multimodal Large Language Models |
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| Improve Vision Language Model Chain-of-thought Reasoning |
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riflezhang/llava-reasoner-dpo/llava_reasoner/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate |
9 Oct 2024 |
shikiw/modality-integration-rate/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Personalized Visual Instruction Tuning |
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sterzhang/pvit/personalize-llava/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time |
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dripnowhy/eta/llava/eval/model_vqa_mmbench_eta.py bae18947b56f2be1 |
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| Treat Visual Tokens as Text? But Your MLLM Only Needs Fewer Efforts to See |
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ZhangAIPI/YOPO_MLLM_Pruning/LLaVA/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference |
6 Oct 2024 |
Gumpest/SparseVLMs/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| EMMA: Efficient Visual Alignment in Multi-Modal LLMs |
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saraghazanfari/emma/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| AVG-LLaVA: A Large Multimodal Model with Adaptive Visual Granularity |
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deeplearnxmu/avg-llava/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Explanation Bottleneck Models |
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| Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs |
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freedomintelligence/trim/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models |
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| Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information |
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banjiuyufen/Recoverable-Compression/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Visual Agents as Fast and Slow Thinkers |
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guangyans/sys2-llava/ROILLaVA/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation |
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| Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs |
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| Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs |
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MBZUAI-LLM/web2code/web2code/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Efficient Large Multi-modal Models via Visual Context Compression |
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Beckschen/LLaVolta/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models |
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| LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors |
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bashlab/llasa/LLaSA/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| SpatialBot: Precise Spatial Understanding with Vision Language Models |
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baai-dcai/spatialbot/bunny/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| VoCo-LLaMA: Towards Vision Compression with Large Language Models |
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Yxxxb/VoCo-LLaMA/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO |
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| Yo'LLaVA: Your Personalized Language and Vision Assistant |
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WisconsinAIVision/YoLLaVA/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models |
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yfzhang114/slime/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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Apache-2.0 (permissive) |
| Multimodal Table Understanding |
12 Jun 2024 |
spursgozmy/table-llava/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM |
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| Matryoshka Query Transformer for Large Vision-Language Models |
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gordonhu608/mqt-llava/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement |
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| LOVA3: Learning to Visual Question Answering, Asking and Assessment |
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| Imp: Highly Capable Large Multimodal Models for Mobile Devices |
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milvlg/imp/imp_llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts |
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shi-labs/cumo/cumo/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Self-Supervised Visual Preference Alignment |
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Kevinz-code/SeVa/seva/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence |
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| Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models |
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atsumiyai/upd/automatic_eval/calculate_scores.py 0e7cf115754e1730 |
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| Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models |
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| Beyond Embeddings: The Promise of Visual Table in Visual Reasoning |
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| Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning |
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deepcs233/visual-cot/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models |
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| Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective |
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| Uncertainty-Aware Evaluation for Vision-Language Models |
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ensec-ai/vlm-uncertainty-bench/data_utils/common_utils.py 25593cfba3f7b483 |
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| Efficient Multimodal Learning from Data-centric Perspective |
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baai-dcai/bunny/bunny/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Aligning Modalities in Vision Large Language Models via Preference Fine-tuning |
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yiyangzhou/povid/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models |
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| MobileVLM V2: Faster and Stronger Baseline for Vision Language Model |
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meituan-automl/mobilevlm/mobilevlm/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation |
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kaistai/prometheus-vision/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs |
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| LLaVA-Phi: Efficient Multi-Modal Assistant with Small Language Model |
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| VCoder: Versatile Vision Encoders for Multimodal Large Language Models |
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shi-labs/vcoder/vcoder_llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator |
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| Compositional Chain-of-Thought Prompting for Large Multimodal Models |
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chancharikmitra/ccot/InstructBLIP-13b/InstructBLIP_MMBench.py bae18947b56f2be1 |
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| LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models |
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| Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision |
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| Octopus: Embodied Vision-Language Programmer from Environmental Feedback |
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| MLLM-DataEngine: An Iterative Refinement Approach for MLLM |
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| StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data |
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icoz69/stablellava/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| Self-attention Does Not Need $O(n^2)$ Memory |
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jihaonew/mm-instruct/llava/eval/model_vqa_mmbench.py bae18947b56f2be1 |
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| arXiv:Xing_Conical_Visual_Concentration_for_Efficient_Large_Vision-Language_Models_CVPR_2025_paper |
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| arXiv:2025.findings-emnlp.1095 |
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| arXiv:2025.findings-acl.865 |
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| arXiv:2025.findings-acl.327 |
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| arXiv:2024.findings-naacl.226 |
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| arXiv:2024.findings-emnlp.775 |
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