:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wu, Tsung-Han, Lee, Heekyung, Ge, Jiaxin, Gonzalez, Joseph E., Darrell, Trevor, Chan, David M.
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2504.13169
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
by: Lee, Heekyung, et al.
Published: (2025)

Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
by: Wu, Tsung-Han, et al.
Published: (2024)

Constantly Improving Image Models Need Constantly Improving Benchmarks
by: Ge, Jiaxin, et al.
Published: (2025)

ALOHa: A New Measure for Hallucination in Captioning Models
by: Petryk, Suzanne, et al.
Published: (2024)

REOrdering Patches Improves Vision Models
by: Kutscher, Declan, et al.
Published: (2025)

Vision-Language Models Create Cross-Modal Task Representations
by: Luo, Grace, et al.
Published: (2024)

CLAIR-A: Leveraging Large Language Models to Judge Audio Captions
by: Wu, Tsung-Han, et al.
Published: (2024)

Recursive Visual Programming
by: Ge, Jiaxin, et al.
Published: (2023)

When Do We Not Need Larger Vision Models?
by: Shi, Baifeng, et al.
Published: (2024)

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios
by: Fan, Jiaqi, et al.
Published: (2024)

Discovering Divergent Representations between Text-to-Image Models
by: Dunlap, Lisa, et al.
Published: (2025)

Dual-Pathway Circuits of Object Hallucination in Vision-Language Models
by: Liu, Jiaxin, et al.
Published: (2026)

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
by: Lian, Long, et al.
Published: (2023)

Resampling Benchmark for Efficient Comprehensive Evaluation of Large Vision-Language Models
by: Suzuki, Teppei, et al.
Published: (2025)

VisionArena: 230K Real World User-VLM Conversations with Preference Labels
by: Chou, Christopher, et al.
Published: (2024)

Vision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning
by: Yin, Shaofeng, et al.
Published: (2026)

AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models
by: Wu, Xiyang, et al.
Published: (2024)

Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models?
by: Geigle, Gregor, et al.
Published: (2024)

Self-Consistency as a Free Lunch: Reducing Hallucinations in Vision-Language Models via Self-Reflection
by: Han, Mingfei, et al.
Published: (2025)

Segmentation-Based Attention Entropy: Detecting and Mitigating Object Hallucinations in Large Vision-Language Models
by: Song, Jiale, et al.
Published: (2026)

Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models
by: Han, Zongbo, et al.
Published: (2024)

MCA-LLaVA: Manhattan Causal Attention for Reducing Hallucination in Large Vision-Language Models
by: Zhao, Qiyan, et al.
Published: (2025)

Reducing Hallucinations in Vision-Language Models via Latent Space Steering
by: Liu, Sheng, et al.
Published: (2024)

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

Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings
by: Agrawal, Aakriti, et al.
Published: (2025)

Analyzing The Language of Visual Tokens
by: Chan, David M., et al.
Published: (2024)

Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
by: Qin, Yiming, et al.
Published: (2025)

HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Token
by: Kogilathota, Sai Akhil, et al.
Published: (2026)

Pose Priors from Language Models
by: Subramanian, Sanjay, et al.
Published: (2024)

DAVE: A VLM Vision Encoder for Document Understanding and Web Agents
by: Huang, Brandon, et al.
Published: (2025)

Collaborative Multi-Mode Pruning for Vision-Language Models
by: Wu, Zimeng, et al.
Published: (2026)

Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
by: Chen, Jiawei, et al.
Published: (2024)

The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting
by: Tomita, Masayo, et al.
Published: (2025)

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

From Plausibility to Verifiability: Risk-Controlled Generative OCR with Vision-Language Models
by: Gong, Weile, et al.
Published: (2026)

Toward More Reliable Artificial Intelligence: Reducing Hallucinations in Vision-Language Models
by: Sanogo, Kassoum, et al.
Published: (2025)

Bridging Visual Representation and Reinforcement Learning from Verifiable Rewards in Large Vision-Language Models
by: Han, Yuhang, et al.
Published: (2026)

Revealing Multi-View Hallucination in Large Vision-Language Models
by: Park, Wooje, et al.
Published: (2026)

Vector Quantized Feature Fields for Fast 3D Semantic Lifting
by: Tang, George, et al.
Published: (2025)

Dual-Process Image Generation
by: Luo, Grace, et al.
Published: (2025)