Cover Image

ARGUS: Hallucination and Omission Evaluation in Video-LLMs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rawal, Ruchit, Shirkavand, Reza, Huang, Heng, Somepalli, Gowthami, Goldstein, Tom
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
by: Shirkavand, Reza, et al.
Published: (2024)

CinePile: A Long Video Question Answering Dataset and Benchmark
by: Rawal, Ruchit, et al.
Published: (2024)

From Pixels to Prose: A Large Dataset of Dense Image Captions
by: Singla, Vasu, et al.
Published: (2024)

FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges
by: Hayes, Kevin David, et al.
Published: (2025)

Measuring Style Similarity in Diffusion Models
by: Somepalli, Gowthami, et al.
Published: (2024)

Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models
by: Ganjdanesh, Alireza, et al.
Published: (2024)

Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs
by: Si, Guangzong, et al.
Published: (2025)

Data-free Defense of Black Box Models Against Adversarial Attacks
by: Nayak, Gaurav Kumar, et al.
Published: (2022)

NOAH: Benchmarking Narrative Prior driven Hallucination and Omission in Video Large Language Models
by: Lee, Kyuho, et al.
Published: (2025)

EventHallusion: Diagnosing Event Hallucinations in Video LLMs
by: Zhang, Jiacheng, et al.
Published: (2024)

Distorted or Fabricated? A Survey on Hallucination in Video LLMs
by: Huang, Yiyang, et al.
Published: (2026)

Diagnosing and Correcting Concept Omission in Multimodal Diffusion Transformers
by: Baek, Kanghyun, et al.
Published: (2026)

Detecting Omissions in Geographic Maps through Computer Vision
by: Nguyen, Phuc D. A., et al.
Published: (2024)

Analysis of Attention in Video Diffusion Transformers
by: Wen, Yuxin, et al.
Published: (2025)

Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineering
by: Cai, Jianfeng, et al.
Published: (2025)

VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models
by: Wang, Yuxuan, et al.
Published: (2024)

Interactive Video Generation via Domain Adaptation
by: Rawal, Ishaan, et al.
Published: (2025)

SmartSight: Mitigating Hallucination in Video-LLMs Without Compromising Video Understanding via Temporal Attention Collapse
by: Sun, Yiming, et al.
Published: (2025)

Vista-LLaMA: Reducing Hallucination in Video Language Models via Equal Distance to Visual Tokens
by: Ma, Fan, et al.
Published: (2023)

Evaluation of Vision-LLMs in Surveillance Video
by: Benschop, Pascal, et al.
Published: (2025)

Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs
by: Nguyen, Dung, et al.
Published: (2025)

Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
by: Lyu, Xinyu, et al.
Published: (2024)

MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations
by: Bae, Kyungho, et al.
Published: (2025)

VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding
by: Li, Chaoyu, et al.
Published: (2024)

Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions
by: Khirodkar, Rawal, et al.
Published: (2024)

EMCompress: Video-LLMs with Endomorphic Multimodal Compression
by: Fan, Zheyu, et al.
Published: (2025)

VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
by: Li, Zongxia, et al.
Published: (2025)

INFACT: A Diagnostic Benchmark for Induced Faithfulness and Factuality Hallucinations in Video-LLMs
by: Yang, Junqi, et al.
Published: (2026)

Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
by: Nguyen, Minh Khoi, et al.
Published: (2026)

Mitigating Hallucination in Multimodal LLMs with Layer Contrastive Decoding
by: Tong, Bingkui, et al.
Published: (2025)

VidHal: Benchmarking Temporal Hallucinations in Vision LLMs
by: Choong, Wey Yeh, et al.
Published: (2024)

Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models
by: Wu, Mingrui, et al.
Published: (2024)

Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation
by: Gao, Hongcheng, et al.
Published: (2025)

Image Generation with a Sphere Encoder
by: Yue, Kaiyu, et al.
Published: (2026)

Hallucination Mitigation Prompts Long-term Video Understanding
by: Sun, Yiwei, et al.
Published: (2024)

MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in Video Scenarios
by: Shi, Yang, et al.
Published: (2025)

A Survey of Multimodal Hallucination Evaluation and Detection
by: Chen, Zhiyuan, et al.
Published: (2025)

Tracing and Mitigating Hallucinations in Multimodal LLMs via Dynamic Attention Localization
by: Yang, Tiancheng, et al.
Published: (2025)

ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models
by: Rawte, Vipula, et al.
Published: (2024)

VERHallu: Evaluating and Mitigating Event Relation Hallucination in Video Large Language Models
by: Zhang, Zefan, et al.
Published: (2026)