Papers with MME
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering (2025.acl-short)
Copied to clipboard
| Challenge: | Current approaches generate visual markers for all questions, generating excessive visual markers. |
| Approach: | They propose a plug-and-play approach that adapts to the complexity of questions . they propose combining fast intuitive judgments with deliberate analytical reasoning . |
| Outcome: | The proposed approach improves performance on four benchmarks on ScienceQA, TextQA, VizWiz, and MME. |
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large vision-language models produce hallucinations that compromise output reliability. |
| Approach: | They propose a dual-stage framework for mitigating hallucinations without performance degradation . they propose semantic-aware component disentanglement and interpretable parameter updates . |
| Outcome: | The proposed model reduces hallucinations by 23.4% while maintaining 97.4% of general generative capability. |
Don’t Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Vision Language Models suffer from hallucinations, attributing incorrect or misleading features to images. |
| Approach: | They propose a test-time approach that recalibrates the influence of blind tokens . they identify blind token by analyzing layer-wise attention distributions over image tokens. |
| Outcome: | The proposed approach reduces hallucinations in large vision language models . it uses a contrastive decoding strategy to balance the influence of blind tokens . |
Towards Text-Image Interleaved Retrieval (2025.acl-long)
Copied to clipboard
Xin Zhang, Ziqi Dai, Yongqi Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Jun Yu, Wenjie Li, Min Zhang
| Challenge: | Existing multimodal information retrieval models rely on single-image inputs . current models use a dense retrieval paradigm, but this approach is not effective . |
| Approach: | They propose a text-image interleaved retrieval task where query and document are interleaves . they adapt off-the-shelf retrievers and build a dense baseline by interleaded multimodal large language model . |
| Outcome: | The proposed model achieves significant improvements over the baseline by substantially fewer visual tokens. |
Taming Object Hallucinations with Verified Atomic Confidence Estimation (2026.eacl-long)
Copied to clipboard
| Challenge: | Multimodal Large Language Models suffer from hallucinations, especially errors in object existence, attributes, or relations. |
| Approach: | They propose a framework that decomposes responses into atomic queries and estimates confidence using self-consistency or self-confidence aggregation. |
| Outcome: | Experiments on five benchmarks show that TACO outperforms direct prompting and Visual Contrastive Decoding and improves confidence calibration. |
To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent multimodal large language models (MLLMs) have attracted widespread attention from both industry and academia due to their potential to handle multiple modalities in a unified framework. |
| Approach: | They propose to classify connectors into feature-preserving and feature-compressing types and categorize tasks into three task types: coarse-grained perception, fine-grain perception, and reasoning. |
| Outcome: | The proposed architectures perform better on tasks with varying granularities than on external fusion architectures. |
DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination (2024.emnlp-main)
Copied to clipboard
| Challenge: | Despite the success of Large Vision-Language Models, they suffer from hallucination. |
| Approach: | They propose a training-free strategy that "D**ive into" the attention of LVLMs to "R**educe" object hallucination by using classification tokens of ViT. |
| Outcome: | The proposed method reduces the impact of outlier tokens on LVLMs . the proposed method is based on LLaVA-1.5, LLvaVA-NeXT and InstructBLIP . |
Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding (2025.findings-emnlp)
Copied to clipboard
Kyungryul Back, Seongbeom Park, Milim Kim, Mincheol Kwon, SangHyeok Lee, Hyunyoung Lee, Junhee Cho, Seunghyun Park, Jinkyu Kim
| Challenge: | Large Vision-Language Models (LVLMs) have shown promising results on multimodal tasks, but remain prone to hallucinations due to their reliance on a single modality or memorizing training data without properly grounding their outputs. |
| Approach: | They propose a training-free, tri-layer contrastive decoding with watermarking that uses a watermark-related question to identify a pivot layer and apply tri-layered contrastive coding to generate the final output. |
| Outcome: | The proposed method reduces hallucinations and generates more visually grounded responses. |
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation (2026.findings-acl)
Copied to clipboard
Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi, Zheming Yuan, Xuxin Cheng, Cao Liu, Ke Zeng, Fengying Xie, Zhiguo Jiang, Haopeng Zhang
| Challenge: | Large vision–language models suffer from object-existence hallucinations when multi-step deliberation decouples from visual evidence. |
| Approach: | They propose a framework that allocates visual computation by uncertainty . they propose highlighting retains global context, while selective zoom-in performs local verification. |
| Outcome: | The proposed framework reduces the complexity of multimodal reasoning by minimizing the operator trade-off. |
FTibSuite: A Comprehensive Resource Suite for Tibetan Vision–Language Modeling (2026.findings-acl)
Copied to clipboard
| Challenge: | FTibSuite provides an end-to-end training-and-evaluation workflow for vision–language models . Tibetan is underserved due to the lack of infrastructure for reproducible training and evaluation. |
| Approach: | They propose a resource-centric workflow for Tibetan VLMs that provides an end-to-end training-and-evaluation workflow and human-verified multimodal annotations. |
| Outcome: | FTibSuite provides an end-to-end training-and-evaluation workflow and human-verified multimodal annotations. |
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent research in large vision-language models has shown promising results, but the issue of hallucination remains. |
| Approach: | They propose an instruction-based method to reduce hallucinations in large vision-language models . they use disturbance instructions to exacerbate hallucinosity in multimodal fusion modules . |
| Outcome: | The proposed method reduces hallucinations in multimodal fusion modules by reducing alignment uncertainty and subtracting hallucines from the original distribution. |
MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding (2025.findings-emnlp)
Copied to clipboard
| Challenge: | LVLMs have shown remarkable performance in visual-language understanding for downstream multimodal tasks. |
| Approach: | They propose a method to alleviate hallucinations by masking the “image heads” in LVLMs . |
| Outcome: | The proposed method alleviates the phenomenon of hallucinations and retains the general capabilities of LVLMs. |