# VCoder: Versatile Vision Encoders for Multimodal Large Language Models

arXiv: https://arxiv.org/abs/2312.14233 · HTML: https://mlsanity.com/paper/2312.14233/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 56 of its 74 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 30 of 56)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)

## Built on by (most cited first, 30 of 42)

- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [A Survey on Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2402.00253/) (2024)
- [SpatialBot: Precise Spatial Understanding with Vision Language Models](https://mlsanity.com/paper/2406.13642/) (2024)
- [AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark](https://mlsanity.com/paper/2410.03051/) (2024)
- [Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent](https://mlsanity.com/paper/2411.02937/) (2024)
- [CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts](https://mlsanity.com/paper/2405.05949/) (2024)
- [CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs](https://mlsanity.com/paper/2408.10433/) (2024)
- [Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models](https://mlsanity.com/paper/2405.17820/) (2024)
- [EVLM: An Efficient Vision-Language Model for Visual Understanding](https://mlsanity.com/paper/2407.14177/) (2024)
- [Wings: Learning Multimodal LLMs without Text-only Forgetting](https://mlsanity.com/paper/2406.03496/) (2024)
- [MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models](https://mlsanity.com/paper/2409.15477/) (2024)
- [Visually Descriptive Language Model for Vector Graphics Reasoning](https://mlsanity.com/paper/2404.06479/) (2024)
- [Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs](https://mlsanity.com/paper/2505.00744/) (2025)
- [Locality Alignment Improves Vision-Language Models](https://mlsanity.com/paper/2410.11087/) (2024)
- [Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2407.11422/) (2024)
- [Unifying Specialized Visual Encoders for Video Language Models](https://mlsanity.com/paper/2501.01426/) (2025)
- [Mitigating Hallucination in Multimodal LLMs with Layer Contrastive Decoding](https://mlsanity.com/paper/2509.25177/) (2025)
- [Elevating Visual Perception in Multimodal LLMs with Visual Embedding Distillation](https://mlsanity.com/paper/2412.09585/) (2024)
- [RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models](https://mlsanity.com/paper/2412.11050/) (2024)
- [Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineering](https://mlsanity.com/paper/2505.12826/) (2025)
- [VEAttack: Downstream-agnostic Vision Encoder Attack against Large Vision Language Models](https://mlsanity.com/paper/2505.17440/) (2025)
- [Fundamentals of Building Autonomous LLM Agents](https://mlsanity.com/paper/2510.09244/) (2025)
- [Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering](https://mlsanity.com/paper/2407.21368/) (2024)
- [SAGE: Training Smart Any-Horizon Agents for Long Video Reasoning with Reinforcement Learning](https://mlsanity.com/paper/2512.13874/) (2025)
- [ReFineVLA: Reasoning-Aware Teacher-Guided Transfer Fine-Tuning](https://mlsanity.com/paper/2505.19080/) (2025)
- [Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models](https://mlsanity.com/paper/2505.19474/) (2025)
- [Measuring Epistemic Humility in Multimodal Large Language Models](https://mlsanity.com/paper/2509.09658/) (2025)
- [Grounding the Score: Explicit Visual Premise Verification for Reliable Vision-Language Process Reward Models](https://mlsanity.com/paper/2603.16253/) (2026)
- [CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models](https://mlsanity.com/paper/2412.16869/) (2024)
- [FaceInsight: A Multimodal Large Language Model for Face Perception](https://mlsanity.com/paper/2504.15624/) (2025)

## Abstract (arXiv)

Humans possess the remarkable skill of Visual Perception, the ability to see and understand the seen, helping them make sense of the visual world and, in turn, reason. Multimodal Large Language Models (MLLM) have recently achieved impressive performance on vision-language tasks ranging from visual question-answering and image captioning to visual reasoning and image generation. However, when prompted to identify or count (perceive) the entities in a given image, existing MLLM systems fail. Working towards developing an accurate MLLM system for perception and reasoning, we propose using Versatile vision enCoders (VCoder) as perception eyes for Multimodal LLMs. We feed the VCoder with perception modalities such as segmentation or depth maps, improving the MLLM's perception abilities. Secondly, we leverage the images from COCO and outputs from off-the-shelf vision perception models to create our COCO Segmentation Text (COST) dataset for training and evaluating MLLMs on the object perception task. Thirdly, we introduce metrics to assess the object perception abilities in MLLMs on our COST dataset. Lastly, we provide extensive experimental evidence proving the VCoder's improved object-level perception skills over existing Multimodal LLMs, including GPT-4V. We open-source our dataset, code, and models to promote research. We open-source our code at https://github.com/SHI-Labs/VCoder