Alternatives and similar repositories for BenchLMM

Users that are interested in BenchLMM are comparing it to the libraries listed below

• AtsuMiyai / UPD
  [ACL2025] Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models
  ☆78Updated 4 months ago
• thunlp / DeepPerception
  DeepPerception: Advancing R1-like Cognitive Visual Perception in MLLMs for Knowledge-Intensive Visual Grounding
  ☆66Updated 3 months ago
• yfzhang114 / LLaVA-Align
  [ACM Multimedia 2025] This is the official repo for Debiasing Large Visual Language Models, including a Post-Hoc debias method and Visual…
  ☆82Updated 7 months ago
• isekai-portal / Link-Context-Learning
  ☆100Updated last year
• yihedeng9 / STIC
  Enhancing Large Vision Language Models with Self-Training on Image Comprehension.
  ☆70Updated last year
• AoiDragon / POPE
  [EMNLP'23] The official GitHub page for ''Evaluating Object Hallucination in Large Vision-Language Models''
  ☆93Updated last month
• YiyangZhou / POVID
  [Arxiv] Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
  ☆88Updated last year
• JiuTian-VL / MoME
  [NeurIPS 2024] MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
  ☆73Updated 5 months ago
• yuhui-zh15 / VLMClassifier
  Official implementation of "Why are Visually-Grounded Language Models Bad at Image Classification?" (NeurIPS 2024)
  ☆91Updated 11 months ago
• heliossun / SQ-LLaVA
  Visual self-questioning for large vision-language assistant.
  ☆44Updated 2 months ago
• Shengcao-Cao / groundLMM
  Emerging Pixel Grounding in Large Multimodal Models Without Grounding Supervision
  ☆40Updated 6 months ago
• foundation-multimodal-models / CAL
  [NeurIPS'24] Official PyTorch Implementation of Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment
  ☆57Updated last year
• DCDmllm / MorphTokens
  ☆43Updated last year
• meetdavidwan / crg
  PyTorch code for "Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training"
  ☆36Updated last year
• AFeng-x / Draw-and-Understand
  [ICLR2025] Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
  ☆88Updated 3 months ago
• icoz69 / StableLLAVA
  Official repo for StableLLAVA
  ☆94Updated last year
• ys-zong / VL-ICL
  [ICLR 2025] VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning
  ☆65Updated 2 weeks ago
• findalexli / mllm-dpo
  [ACL 2024] Multi-modal preference alignment remedies regression of visual instruction tuning on language model
  ☆47Updated 10 months ago
• palchenli / VL-Instruction-Tuning
  ☆91Updated last year
• Yuqifan1117 / HalluciDoctor
  HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data (Accepted by CVPR 2024)
  ☆48Updated last year
• patrick-tssn / VideoHallucer
  VideoHallucer, The first comprehensive benchmark for hallucination detection in large video-language models (LVLMs)
  ☆37Updated 6 months ago
• cliangyu / Cola
  [NeurIPS2023] Official implementation of the paper "Large Language Models are Visual Reasoning Coordinators"
  ☆104Updated last year
• zhangquanchen / VisRL
  [ICCV 2025] VisRL: Intention-Driven Visual Perception via Reinforced Reasoning
  ☆39Updated 3 months ago
• YiyangZhou / CSR
  [NeurIPS 2024] Calibrated Self-Rewarding Vision Language Models
  ☆80Updated last year
• foundation-multimodal-models / CAPTURE
  ☆76Updated last year
• RenShuhuai-Andy / TESTA
  [EMNLP 2023] TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
  ☆50Updated last year
• dvlab-research / Prompt-Highlighter
  [CVPR 2024] Prompt Highlighter: Interactive Control for Multi-Modal LLMs
  ☆153Updated last year
• opendatalab / HA-DPO
  Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization
  ☆93Updated last year
• whwu95 / FreeVA
  FreeVA: Offline MLLM as Training-Free Video Assistant
  ☆63Updated last year
• MMStar-Benchmark / MMStar
  [NeurIPS 2024] This repo contains evaluation code for the paper "Are We on the Right Way for Evaluating Large Vision-Language Models"
  ☆195Updated last year