Demystifying the Visual Quality Paradox in Multimodal Large Language
  Models
Recent Multimodal Large Language Models (MLLMs) excel on benchmark
vision-language tasks, yet little is known about how input visual quality
shapes their responses. Does higher perceptual quality of images already
translate to better MLLM understanding? We conduct the first systematic study
spanning leading MLLMs and a suite of vision-language benchmarks, applying
controlled degradations and stylistic shifts to each image. Surprisingly, we
uncover a visual-quality paradox: model, task, and even individual-instance
performance can improve when images deviate from human-perceived fidelity.
Off-the-shelf restoration pipelines fail to reconcile these idiosyncratic
preferences. To close the gap, we introduce Visual-Quality Test-Time Tuning
(VQ-TTT)-a lightweight adaptation module that: (1) inserts a learnable,
low-rank kernel before the frozen vision encoder to modulate frequency content;
and (2) fine-tunes only shallow vision-encoder layers via LoRA. VQ-TTT
dynamically adjusts each input image in a single forward pass, aligning it with
task-specific model preferences. Across the evaluated MLLMs and all datasets,
VQ-TTT lifts significant average accuracy, with no external models, cached
features, or extra training data. These findings redefine ``better'' visual
inputs for MLLMs and highlight the need for adaptive, rather than universally
``clean'', imagery, in the new era of AI being the main data customer.
6859a3f90e4ad7e219758453
Shuo Xing
claimed_verified
2025-06-25T08:09:30.287Z
62df9ee3c7e6f74fc92a1351
/avatars/2a4f60ecc2fc4be2a8ac21c3f2ceb3cc.svg
Shuo Xing
user
shuoxing
6859a3f90e4ad7e219758454
Lanqing Guo
admin_assigned
2025-06-24T17:44:39.718Z
663b9ca3077aba104d588da4
/avatars/1de2d00e896ed609ffbdb4f8f32d5cc7.svg
GUO LANQING
user
Smurfsguo
6859a3f90e4ad7e219758455
Hongyuan Hua
admin_assigned
2025-06-24T17:44:10.434Z
64926a1d0b0ad03ceb6dd80e
/avatars/f4cf771734d0c3d3747f74ccf47d241d.svg
hongyuan hua
user
hongyuanhua
6859a3f90e4ad7e219758456
Seoyoung Lee
6859a3f90e4ad7e219758457
Peiran Li
6859a3f90e4ad7e219758458
Yufei Wang
6859a3f90e4ad7e219758459
Zhangyang Wang
6859a3f90e4ad7e21975845a
Zhengzhong Tu
admin_assigned
2025-06-24T17:44:17.149Z
62548d5fef3debb2ddf91217
/avatars/14975b45568f9c399c92c3986b6ce83e.svg
Zhengzhong Tu
user
vztu
Demystifying the Visual Quality Paradox in Multimodal Large Language
  Models
Recent Multimodal Large Language Models (MLLMs) excel on benchmark
vision-language tasks, yet little is known about how input visual quality
shapes their responses. Does higher perceptual quality of images already
translate to better MLLM understanding? We conduct the first systematic study
spanning leading MLLMs and a suite of vision-language benchmarks, applying
controlled degradations and stylistic shifts to each image. Surprisingly, we
uncover a visual-quality paradox: model, task, and even individual-instance
performance can improve when images deviate from human-perceived fidelity.
Off-the-shelf restoration pipelines fail to reconcile these idiosyncratic
preferences. To close the gap, we introduce Visual-Quality Test-Time Tuning
(VQ-TTT)-a lightweight adaptation module that: (1) inserts a learnable,
low-rank kernel before the frozen vision encoder to modulate frequency content;
and (2) fine-tunes only shallow vision-encoder layers via LoRA. VQ-TTT
dynamically adjusts each input image in a single forward pass, aligning it with
task-specific model preferences. Across the evaluated MLLMs and all datasets,
VQ-TTT lifts significant average accuracy, with no external models, cached
features, or extra training data. These findings redefine ``better'' visual
inputs for MLLMs and highlight the need for adaptive, rather than universally
``clean'', imagery, in the new era of AI being the main data customer.
Visual-Quality Test-Time Tuning (VQ-TTT) improves Multimodal Large Language Models (MLLMs) performance on vision-language tasks by dynamically adjusting input images, demonstrating the importance of adaptive rather than universally clean imagery.
Multimodal Large Language Models (MLLMs) vision-language tasks visual-quality paradox Visual-Quality Test-Time Tuning (VQ-TTT) learnable low-rank kernel vision encoder LoRA task-specific model preferences