Grounding-IQA: Grounding Multimodal Language Model for Image Quality Assessment
Authors: Zheng Chen
Xun Zhang
Wenbo Li
Renjing Pei
Fenglong Song
Xiongkuo Min
Xiaohong Liu
Xin Yuan
Yong Guo
Yulun Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Grounding
MLLM
IQA
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: grounding multimodal language model for IQA

Abstract: The development of multimodal large language models (MLLMs) enables the evaluation of image quality through natural language descriptions. This advancement allows for more detailed assessments. However, these MLLM-based IQA methods primarily rely on general contextual descriptions, sometimes limiting fine-grained quality assessment. To address this limitation, we introduce a new image quality assessment (IQA) task paradigm, **grounding-IQA**. This paradigm integrates multimodal referring and grounding with IQA to realize more fine-grained quality perception, thereby extending existing IQA. Specifically, grounding-IQA comprises two subtasks: grounding-IQA-description (GIQA-DES) and visual question answering (GIQA-VQA). GIQA-DES involves detailed descriptions with precise locations (e.g., bounding boxes), while GIQA-VQA focuses on quality QA for local regions. To realize grounding-IQA, we construct a corresponding dataset, GIQA-160K, through our proposed automated annotation pipeline. Furthermore, we develop a well-designed benchmark, GIQA-Bench. The benchmark evaluates the grounding-IQA performance from three perspectives: description quality, VQA accuracy, and grounding precision. Experiments demonstrate that our proposed method facilitates the more fine-grained IQA application. Code: https://github.com/zhengchen1999/Grounding-IQA.

Published as a conference paper at ICLR 2026
GROUNDING-IQA: GROUNDING MULTIMODAL LAN- GUAGE MODEL FOR IMAGE QUALITY ASSESSMENT
Zheng Chen', Xun Zhang', Wenbo Li², Renjing Pei Fenglong Song? Xiongkuo Min' Xiaohong Liu', Xin Yuan", Yong Guo Yulun Zhang 'Shanghai Jiao Tong University. PJoy Future Academy, HHuawi Noah's Ark Lab, *Westlake University, SHuawei
ABSTRACT
The development of multimodal large language models (MLLMs) enables the evaluation of image quality through natural language descriptions. This advance- ment allows for more detailed assessments. However, these MLLM-base IQA methods primarily rely general contextual descriptions, sometimes limiting fine-grained quality assessment. To address this limitation, we introduce a new im- age quality assessment (IQA) task paradigm, grounding-IQA. This paradigm in- tegrates multimodal referring and grounding with IQA to realize more tine-grained quality perception, thereby extending existing IQA. Specifically, grounding- IQA comprises two subtasks:
(GIQA-DES) and visual question answering (GIQA-VQA). GIQA-DES involves detailed descriptions with precise locations (e.g.. bounding boxes), while GIQA-VQA focuses on qual- ity QA for local regions. To realize grounding-IQA. we construct a correspond- ing dataset, GIQA-160K, through our proposed automated annotation pipeline. Furthermore, we develop a well-designed benchmark, GIQA-Bence The bench- mark evaluates the grounding-IQA performance from three perspectives: de- scription quality, VQA accuracy, and grounding precision. Experiments demon- strate that our proposed method facilitates the more fine- IQA application. Code: made::https:/// / liggttthn thub. com/ zhengchen 2099//roundirgg ng- IQA
INTRODUCTION
Image quality assessment (IQA) seeks evalu- ate image quality in alignment with human per- ception. As a fundamental task in low-level vi- sion, IQA is critical across multiple fields, e.g., image processing (Zhang et al. |018 Lin al. 2019). media transmission (Ying et al.) (200)) and generative artificial intelligence (Li et al. 2023). However, this task is challenging since the human visual system is inherently subjec- tive and complex to model (Wang et al. (004))
IQA (DES)
Grounding (VGA)
IQA (2E8)
TageRteai
Grounding (VGA) mau
Ace (V)
To enhance evaluation precision, substantial re- search efforts continue to be dedicated to this
Goundigng wap.aaantuoo owt
Grounding (DEB)
IQA (VGA)
TegRecall
area (Mittal et al.] 2012a Ding et al., 2020)
Grounding (DES)
miel
Wang et al. 2023 Wu et al. 2024b).
Figure/ 1: Performance comparisons GIQA- Bench. Our proposed grounding GPT effectively combines grounding and IQA.
Traditional IQA methods employ handcrafted metrics to estimate quality scores (Wang al.
2004 Mittal et al. 2012b). With advancements in deep neural networks, learning specific priors
from large datasets enables more accurate score predictions (Kang et al.] 2014] Bosse et al., 2017 Jinjin et al.) 2020 Ke et al. 2021). Nevertheless, score-based IQA methods face challenges in complex scenarios. such cases, image quality is influenced by multiple factors that single score cannot effectively express (You et al. (224c)). Recently, the emergence of multimodal large language models (MLLMs) (Liu et al. [2223] Peng et al. 20244] Ye et al. (204)) with strong visual and linguis- tic perception capabilities provides alternative to acore-based IQA (Wu et al., 2024a]e). These models achieve more detailed and accurate image assessments through description and reasoning.
"Corresponding author; Yulun Zhang, yulun 100@@000@ll. gmail.ccomm

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Published as a conference paper at ICLR 2026
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Figure 2: Grounding- -QA combines referring and grounding with IQA. (a) GIQA-DES: Quality de- scription include precise locations (i.e., bounding boxes). (b) GIQA- The question (referring, bottom instance) or answer (grounding, top instance) contains locations.
However, current MLLM- IQA methods (Wu et al., 2024b You al. 2024b) primarily rely on general contextual descriptions, which sometimes limits fine- graind quality assessments. For in- stance, in Fig. 2p. the existing method (i.e., Q-Instruct (Wu et al., 2024b)) describes the objects/areas affecting image quality through language, but cannot provide precise location information. More- over, in Fig. 2p. for local perception, the language referring may not accurately pinpoint the target, leading to bias. These limitations restrict the application of MLLMs in comprehensive low-level perception and understanding, especially for fine -ee-graindd cases.
To address these challenges and unleash the potential of MLLMs in fine-grained image quality understanding, we introduce grounding-IQA4 This is a novel IQA task paradigm that integrates mul- timodal referring (position in) and grounding (position out) (Mao et al. 2016 Chen et al. 2023 Peng et al. 2024) with image quality assessment. This new paradigm can serve as an extension and enhancement to existing IQA methods. Specifically, we categorize grounding-IQA into two sub- (GIQA-DÉSS) As illustrated in Fig. 2h. this task requires generating descriptive assessments of image quality while providing precise locations (i.e., bound- ing boxes) for important objects/regions impacting quality. (2) Grounding-IQU Visual Question Answering (GIQA-VQA). As shown in Fig. Dp this task involves QA about low-level attributes of images, especially regarding local objects. It includes addressing questions with specific coordinates (referring) or providing answers with precise positions (grounding).
tasks: (1) Grounding-IQ2e IeA---QQr-Dettttii
Since existing datasets can not realize grounding-IQA well (Liu et al., 2023 You et al., 2024a; Wu jet al., 2024b), we construct a new dataset, GIQA-160K, based on the proposed paradigm. This dataset can enhance the grounding-IQA capabilities of current MLLMs. The dataset comprises 160K inttrcction-uningg data with 40K images from diverse domains. Specifically, the dataset cor- responds to two sub-tasks: GIQA-DES includes 60K corresponding data, and GIQA- VQA contains 100K related data. To construct the corresponding dataset, we design an automated annotation pipeline. The automated pipeline generates the G1QA-160K through the public IQA dataset (Wul et al. 20246) You et al. 2024b) (with the human-annotated deseription). (1) For GIQA-DES. The task includes detailed descriptions with coordinates. We generate the data through advanced vi- sion (Liu et al. (202cc) and language (Dubey et al.] (0224) models. Through these models, we extract and filter objects and corresponding coordinates from existing descriptions and images. Meanwhile, coordinates are expressed in natural language and attached to text. This avoids extra specialized tokens and ensures data compatibility. (2) For GIQA-VQA. Inspired by previous work (Wu al.) 2024b) You et al. 2024a] Li et al., 2024), we construct the required data from the detailed descrip- tions in GIQA-DES via the L.M. We use specific QA templates (i.e., "Yes/No", abbreviated as Y; "What/How/Why" abbreviated as W) and emphasize location-specii objects to generate appropri- ate data. The coordinates are also combined with the generated QA.
Fine-tuning on the GIQA-160K dataset enables existing pre-trained MLLMs to achieve impressive Prounding-IQA capabilities. As shown in Fig. 2 the fine-tuned model can ground key objects af- fecting image quality, and perform more fine-grained assessments based reference coordinates. Moreover, to comprehensively evaluate the model performance on the grounding- IQA task, we pro- pose a well-designed benchmark, GIQA-Bench. This benchmark includes 100 varying types and
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Published as a conference paper at ICLR 2026
quality images, corresponding to 100 GIQA-Des and 150 GIQA-VQA test samples. Each sample is annotated over multiple rounds by at least three experts. We quantitatively assess grounding-IQA
performance in three aspects: (1) assessment description quality (i.e., BLEU@4, LLM-Score); (2)
VQA accuracy (i.e., Accuracy): and (3) grounding precision (ie., mloU, Tag-Recall, We test recent
MLLMs,, with results shown in Fig. T Observations indicate significant improvement in grounding- IQA after fine-tuning with GIQA- BIQA-160K. Overall, our contributions are threefold:
We introduce multimodal referring and grounding into IQA, establishing new IQA paradigm, grounding-IQA, for fine-grained quality perception and assessment.
We construct high-quality dataset, GIQA-160K, with an automated annotation pipeline. The dataset is versatile and suitable for fine-tuning existing MLLMs.
We propose a high-quality benchmark, GIQA-Bench, to comprehensively evaluate the model performance grounding -QA from three aspects.
2 RELATED WORK
2.1 IMAGE QUALITY ASSESSMENT
Score-based Methods. Most current IQA methods are score-based. Early IQA approaches com- pute scores through handcrafted image data metrics (Wang et al. 2004 Moorthy & Bovik 201T] Mittal et al. 2012a). However, these methods show gap in quality perception compared to human judgment and are unsuitable for complex scenarios. With the development of the neural network, leariin-basee IQA methods have gradually become mainstream (Yang et al. 2022, Chen et al.) 2024a (hhin et al. 2024). These methods leverage data-driven training to achieve more accurate quality assessments. For example, LPIPS (Zhang et al.|201 [0188 8) applies the convolutional neural net- work to compute scores. Moreover, meta-learning (Zhu et al., 2020). multimodal models (Wang ct al. 2023 Zhang et al. 2023c), and graph neural networks (Sun et al. (2022) have been adopted to further improve IQA. However, score-based IQA methods face limitations in complex scenarios. The simple score cannot effectively represent the multiple aspects affecting image quality.
MLLM-based Methods. Multimodal large language models (MLLMs) exhibit remarkable multi- modal (language/vision) understanding by integrating visual modules into LLMs (Liu et al., [023 Zhang et al. (2023a; Jiang et al.] 2024). MLLMS achieve outstanding performance in various mul- timodal tasks, including visual question answering and image captioning. Recently, several studies have also demonstrated the potential of MLLMs in low-level visual perception and assessment (Wu et al. 2024b You et al. 2024b b. Wu et al. 2024d| Chen et al. 2024b). For instance, Q-Instruct (Wu et al. 2024b constructs a multimodal dataset to enhance. Q-Align (Wu et al. 2024c) guides MILMS in scoring by defining discrete text-based levels. DepictQA (You et
enables quality com- parison and reasoning based on reference images. These approaches advance the application of MLLMs in IQA, achieving more accurate assessments. Nevertheless, these models primarily rely on contextual descriptions, and face limitations in fine-grained applications, egg., local perception.
2.2 MULTIMODAL REFERRING AND GROUNDING
Multimodal spatial perception involves referring and grounding. Referring requires the model to understand the specific region based on position input, egg., region-level captioning (Krahmer & Van Deemter 2012: Zellers al. 2019). Grounding, the other hand, involves the model describ- ing the region by outputting position, e.g., referring expression comprehension (Kazemzadeh et al.)
2014 Luo & Shakhnarovich/ 2017). Currently, MLLMs perform impressively in spatial perception, further advancing these tasks. Some methods focus on grounding, achieving complex reasoning (Lai al.) (224)) or multi-object (Ren al., (024) segmentation. Mcanwhile, other approaches, e.g.,
GPT4Rol (Zhang et al. 2023b), emphasize understanding specific regions (referring). Furthermore, some works unify referring and grounding (Chen et al. 2023] Li et al. [024] |Rahhed et al. 2024] Peng et al.) 2024. You et al., (202a)). Additionally, in IQA, Q-Ground (Chen et al. 2024b) achieves degradation region grounding but lacks referring capabilities. In contrast, our Grounding-IQA inte- grates multimodal referring and grounding with IQA to enhance quality perception.
METHOD
In this section, introduce the newly defined IQA paradigm, grounding IQA. The content in- cludes: (1) definition of paradigm and two subtasks. Sec.B.II, (2) data construction pipeline, Sec. (3) details of GIQA-160K, Sec. 3.3 (4) benchmark for grounding-IQA. Sec. 3.4
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Published as a conference paper at ICLR 2026
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Figure 3: The illustration of the automated annotation pipeline. (a) GIQA-DES Pipeline: Constructs the answer from the given image and description via a four-stage process, while the question comes from a predefined question pool. (b) GIQA-VOU Pipeline: Generates the corresponding QA data utilizing descriptions from GIQA-DES and the LLM (Llama3 (Dubey et al. 2024)).
3.1 GROUNDING-IQA
As analyzed above, existing MLLL-b IQA methods leverage descriptions to enable more curate and detailed quality assessments. However, these methods remain limited performing fine-grained evaluations, as in Fig. 2 Inspired by work multimodal referring and grounding, we believe that spatial perception is key to achieving more fine-grained assessments, Therefore, to further unlock the potential of MILMs, paradigm combines referring and grounding with IQA to enable more precise and flexible quality assessments. Specifically, grounding-IQQ IQA should include the two sub-tasks/capp habb-asks/caaabbitieess grounding- IQA-description (GIQA-DES) and grounding- -oadiiin------suuall question answering (GIQA-VQA).
introduce
IQA paradigm, grounding-IQA. This
GIQA-DES. The task requires the model provide detailed description of image quality. Ad- ditionally, it needs accurate location information (e.g., bounding box) for key objects/regions that impact image quality, as shown in Fig. pp. This corresponds the fact that humans consider not only the overall quality (e.g., image clarity) but also the quality of specific objects or locations when as- sessing image quality. Meanwhile, accurate location information also enables targeted information for downstream tasks (e.g., image editing). This task is similar to grounded image captioning (Zhou) et al. 2020), but places greater emphasis on low-level attributes. While some MLLMS (Chen al., 2023 Peng et al. 2024 Li et al., 2024) perform well in grounded image captioning, they still strug- gle with quality perception. We demonstrated it in Sec. 4.3
GIQA-VQA. The second task focuses on the question-answering ability in low-level perception, particularly for local objects. Corresponding to multimodal referring and grounding, this task can be divided into two scenarios. Referring: querying low-level attributes in the specified region (input position), as shown in Fig.5p. Grounding: providing answers that include specific locations (outpu position) based on the question, depicted in Fig. [bp. These two scenarios are related to region captioning (Zhou et al. 2020) and phrase grounding (Zhou et al. 22020), respectively. However, like GIQA-DES, GIQA-VQA involves quality perception, which is challenging for current MLLMs.
3.2 AUTOMATED ANNOTATION PIPELINE
Data is essential for achieving Grounding-IQA. Therefore, construct automated annotation pipeline to generate data (i.e. GIQA-160K). This pipeline leverages public IQA datasets (Wu al., 2024b; You et al.
2024b) that contain human -nnotttdd descriptions. Following previous schemes (Liu et al. 2023 Ye et al. 2024). the data format is (image, question, answer). The image is the evaluation target. Depending on the sub-task, the question and answer fields may include precise coordinates (i.e., bounding box), in addition to text. The illustration of the
whole pipeline is Fig. 3 Besides, more details are provided in the supplementary material.
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Patch numbers Published as a conference paper at ICLR 2026
For GIQA-DES. In this task, the question is relatively fixed, as in Fig. 5h. For each data point, the question is randomly selected from the question pool with 15 similar questions. For the answer, it is detailed description with coordinates. We construct it via a four-stage process from existing images and associated description, as illustrated in Fig. 3 (1) Stage-
Object Nome
Description Phrase
Figure 4: Utilizing the description phrase Ty ("thhe man wearing a white t-shirt") yields more accu- rate detection than applying object name ("man").
I: object tag extraction; (2) Stage-2: bounding box detection; (3) Stage-3: box refinement (fil- ter and merge); and (4) Stage-4: transformation and fusion. Each stage is detailed below.
Stage-1: Object Tag Extraction. Firstly, we apply the advanced I. LLM, ie., Llama3 (Dubey et al., 2024). to extract key objects (e.g., "billiard table" in Fig. (3)) from the given descriptions. Each object is assigned a three-tuple form tag: (T,, Ty. Te The T, is the object description phrase (sometimes same as name); Ty denotes the quality of object (e.g., "clear"); Te represents the object effect image quality (i.e,, "no impact", "positive", from the description, with T, and Ty used in later stages. The Te item enables us to filter out non- critical objects (e.g., "image", which refers to the whole). This explicit effect classification, similar to chain-of-thought (CoTT), can reduce hallucinations.
"negative"). All tag items are inferred
Stage-2: Bounding Box Detection. Then, we detect bounding boxes for the extracted objects from the image. To accomplish this, we utilize the sateeoffthheal object detection model, Grounding DINO (Liu al., 2024c). Since multiple same-category objects may appear in one image, we utilize the 7 generated Stage-1 rather than the object name for detection. For instance, in Fig. E the object name is "man". and Tr is "the man wearing a white t-shirt". Leveraging "man" detects two objects (left case), while using Tr can achieve the more precise result (right case).
Stage-3: Box Refinement. Although Stage-2 adopts Tr limit the detection range, multiple boxes may still exist. In some cases, multiple boxes may contain the wrong target. Through observations, most detection errors arise from the detection model inability to distinguish objects of same class with different quality. For instance, in Fig. (3,, for "hands", the key (reduce image quality) is the blurry one, and the other is irrelevant. To address this problem, we design the IQA-Filter algorithm (Alg. D.. inputting each box patch and asking: "Is the image quality is <T,>?", with Ty from Stage-1. We check all boxes in maggll--ojec--uulpll--aa-ee
We use the MLLM- --seeddaed IQA method, Q-Instruct, to verify detected bounding boxes by
and remove those with "No" response.
Furthermore, in some cases, multiple small or overlapping targets correspond to the same object. While these detections accurate, an excess of targets may increase the learning difficulty for MLLMs. To address this issue, we propose the Box-Merge algorithm (Alg. [T. We merge boxes that satisfy the normalized area threshold Ta (set 0.256), and the overlap threshold T, (set to 95%).
Stage-4:
Transformation and Fusion. Finally, we integrate the extracted and filtered boxes into the original descriptions construct the answer. To avoid introducing extra specialized tokens for box representation, we treat box coordinates as regular text tokens, attaching them to the text in the interleaved format: (oogeectrreioonnbbundiii box)".
Moreover, bounding boxes are typically represented by normalized corner coordinates: (11. 91, 22, V2). When the coordinate values are rounded two decimal places (e.g., (0.01, 0.02, 0.03, 0.04)), representing box requires 21 tokens. Inspired by previous work You et al.) 2024a Peng et al. 2024), we discretize the coordinates for simplicity. We divide the image into x grids and numbering grids from top-left to bottom-right:
then represent the lop-left and bottom-right coordinates of the box:
(1) where idx; and idx, denotes the coordinates. The box can be represented as (idx,, idx,). Accord- ingly, we remap the discrete coordinates back to a continuous format using the centre coordinates:
idx; = y1 m n + 21 n, idx, = 1/2 m n2 82 n,
2 = (idx/%n= + 0.5)/n, sí = (idx//n + 0.5)/m,
2/2 = (idx, %n+ + 0.5)/n, 3/2 = (idx,/n+ 0.5)/m,
where new coordinates is (21, 1/112111/0 Z/y. 2/2). Though the discretization reduces coordinate precision, it effectively simplifies the representation. In our dataset, we set n=m=20, requiring at most 9 tokens.
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Published as a conference paper at ICLR 2026
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Some instances from the GIQA-160K, involving subtasks: GIQA-DES and GIQA-VQA.
Finally, the answee is a natural language description with precise coordinates, as shown in Fig. Bh.
Algorithm IQA-Filter & Box- MMege
Input:
target image I, object bounding boxes B, object
quality Ty. area threshold Ta, overlap threshold T.
For GIQA-VQA. The task re-
Output: the refined bounding boxes
quires that the question or answer relate low-level at- tributes and include explicit spa-
3: Init: R <- 0
D IQA-Filter: filter boxes by quality query
4: for b E B do
tial information (i.e., bounding
5: p f patch((, b); q <Is the image quality <Tn>?"
boxes). Inspired by previous work (Wu et al., 2024b You al. 2024a Li et al. 2 we ap- ply the LLM (i.e., Llama3 (Dubey) ett al. )024 to generate the cor- responding QA pairs from the de- scriptions in GIQA-DES (depicted
6:
if Q-Insttruc((p, q) "Yes" then
7:
R F- U (b)
8: end if
9: end for
D Box-Merge: merge overlapped boxes
10:
for i = 0; < |R - + 1 do
11:
j j+i+1
in Fig. (b.. We use specific tem- plates to generate diverse QA. De-
12:
while j |R] do
13:
if (R(l(
Ta and is-touch [i]) RD)) or
tails are as follows:
coverage-ratio [1] R() > T, then
14:
R[i + merge R[. R RD); R + R [R[}}
(I) Binary Questions ("Yes/No"): Answers are limited to "Yes" or "No". The "Yes" answer corre- sponds to questions inferred di- rectly from the description. Con-
15:
else
16:
j j+i+1 end if
17:
18: end while
19:
end for
versely, quality questions that can-
20: return R
not be inferred are answered "No".
(2) Open-ended Questions ("Wha/Why/Hoow"): These questions address low-level attributes or related context (e.g., "What types of distortion?"); cause analysis (e.g.. "Why the image quality is poor?"); perceptual degree (e.g.. "How is clarity?"). All answers are inferred from the description and given as short phrases (e.g., "Noise" and "Medium").
Meanwhile, we supply the LLM with the names of key objects/regions (with bounding boxes), constraining the QA to relate to relevant entities. We also use keyword detection to filter out any unrelated QA pairs. Finally, we incorporate bounding box information into the generated QA pairs, forming the corresponding question and answer.
3.3 G1QA-160K
We construct our grounding-IQX. dataset, GIQA-160K, utilizing the automated annotation pipeline, from existing public datasets (Wu et al. 2024b; You al. 2024b). Figure Showw some instances.
shows
Data Source. To build our dataset, we require two types of data: diverse images and their corre- sponding detailed quality descriptions. Currently, two public datasets, Q-Pathway (Wu et al. 2024bb and DQ-495K (You et al. (2024b). meet our requirements. For O-Pathway, we select in- wiil im- ages (KonIQ-1OK (Hosu al. (2020), SPAQ (Fang et al. 2020), LIVE-FB (Ying al. 2020). and LIVE-itw (Ghadiyaram & Bovik 2015)) and Al-generated images (AGIQA-3K (Li et al. 2023 and ImageRewardDB Xu al. 2024)). ). along with their professionally auman-annotatedd texts. The total
image-text pairs is 53K. For DQ- 27K artificially degraded images (from KADIS-700K (Lin
et al. )020 are paired with human- -anott-e descriptive texts.

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Dataset Statistic. Utilizing the above raw data (80K image-text pairs), we construct a dataset with 167,657 instruction-tuning samples and 42,960 images. Dataset statistics are shown in Tab. [T, For GIQA-DESS, we generate 66,689 detailed quality descriptions with coordinates. The GIQA-VQA contains 100,968 question-
Table 1: Statistics information of the proposed
datasets. DES: GIQA-DES; VQA: GIQA-VQA.
Dataset
Image
Total
DES
(Y)
VQA (W)
GIQA-160K
42,960
167.657
66,689
50,484
50,484
GIQA-Bench
100
250
100
90
60
answer pairs. For GIQA-VQA, to balance question types, we randomly filter to maintain an equal amount of "Yes/No" and "What/Which/How" questions (50,484 each). Additionally, we ensured a balanced distribution between "Yes" and "No" responses, with 25,242 samples in each category.
3.4 GIQA- -BECH
We construct a high-quality benchmark, GIQA-Bench, to evaluate the model grounding- -quA per- formance, detailing its data statistics and evaluation criteria.
Bench Statistic. The GIQA-Bench includes 100 images of various types and quality, which are not included in GIQA-160K. We create 100 GIQA-DES and 150 GIQA-VQA test samples based on these images. Among the 150 GIQA-VQA data, 90 are of the "Yes/No" questions ("Yes": 35; "No": 55), and 60 are Whht/Which/Hoow" questions ("What": 30: "Why": 18; "How": 12).
The descriptions for GIQA-DES are from Q-Pathway and adjusted, with key objects and bounding boxes manually determined. GIQA-VQA questions are generated by the annotation pipeline and further refined and answered by humans. Each sample is annotated in multiple rounds by least three experts with relevant expertise in a controlled laboratory environment to ensure accuracy.
Evaluation Criteria. We evaluate the grounding-TQA capabilities from three perspectives: descrip- tion quality, VQA accuracy, and grounding precision. For all metrics, higher values are better.
(1) Description Quality. Assess GIQA-DES performance in quality descriptions. We compare the generated description to the ground truth, excluding coordinates. We apply the image captioning metrie; BLEU@4. We also employ the LLM (Llama3 (Dubey et al. (004g) to provide a score from 0 to 4 (higher better), based on the relevance between the description and the ground truth. For clarity, the final score is scaled proportionally from 0 to 100. We denote the score as the L.MM-Score
(2) VQA Accuracy. Evaluate GIQA- performance
quality VQA. For "Yes/No" questions, accuracy is determined by matching with the word "Yes" or "No". For "Wha/Which/How", we use LLM to calculate accuracy. The LLM scores the model response from 0 to (higher is better) based on the question and correct answer. The score is normalized to 0-1. We denote the accuracy of "Yes/No" as Acc (Y), "What/Whic/Howw" as Acc (W), and overall accuracy as Ace (Total).
(3) Grounding Precision. Measure the grounding performance for both GIQA-DES and GIQA- VQA. We use cagesory- mean Intersection over Union (mloU) to evaluate box quality. We also define Tag-Recall to assess category- -specfff grounding capabilities. In Teg-Recall, result is true positive only if both the loU and object name similarity exceeds a 0.5 threshold. For faimess, the bounding box is represented by the normalized corner coordinate.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Implementation Details. We conduct experiments on four pre-trained MLLM models: LLaVA- v1.5-7B ct. 20244). -LaVA-v1.5-133 (Liu et al. (024a), LLaVA-v1.6-7B (Liu et al. 2024b), and mPLUG- LLUU--W11-77 7B (Ye et al. 2024). These models involve different versions, sizes, and archi- tectures. The models are fine-tuned on our proposed GIQA-160K dataset using supervised fine- tuning. We evaluate their performance on grounding- IQA using the GIQA-Bench. Details about the training/testing datasets and evaluation criteria are provided in Secs.
and
and 3.4
Training Settings. We adopt cross-entropy loss for full fine-tuning, following previous meth- ods (Wu et al.) 2024b; Liu et al. 2023 Ye et al. 2024). The optimizer is AdamW (Loshchilov) et al. 2018). with 31 0.9 and Bg 0.999. We apply the cosine decay scheduler with an initial learn- ing rate of 2 10 and warmup ratio of 0.03. The batch size is set to 64, and the epoch is 2. Other hyper-parameters parameters follow the default settings of each model. Experiments are implemented with PyTorch (Paszke al. 2019) on four Nvidia A 1000-80G GPUs.
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Table Ablation study on box optimization (refinement and representation) in the automated anno- tation pipeline. We conduct experiments on the GIQA-DES task.
(a) Box refinement.
(b) Box representation.
Method
mloU
Tag-Recall
BLEUE4
LLM-Score
Method
mloU
Tag-Rocall
BLEU@4
LLM-Score
Baseline
3.62
48.25
Baseline
N/A
3.62
48.25
Raw Box
0.5624
0,5045
20.97
61.00
Norm Coord
0,6046
0,5490
22.03
61.00
23.67
61.75
Disc-Coord
0.5851
0.5497
61.75
Table 3: Ablation study on multi-task training. The baseline is the pre-trained model, mPLUG- Ow12-7B, without fine-tuning.
Box Area Distribution
G10A
GI0A-168 160K: Raw
Deisty a le
GIOA- 160K: Ref
GIQA-DES
GIQA-VQA
Method
Tag-Rocall
LLM-Score
Tag-Recall
Acc (Total)
Baseline
N/A
48,25
NIA
0.5633
Ooly-DES
0.5497
61.75
0,5577
0.5900
Normaized
Only-VQA
0,3283
38,50
0.4872
0.7217
Figure 6: Box area distribution of GIQA-160K
GIQA-160K
0,5474
63.00
0.7372
0.7417
(Raw and Ref) and GIQA-Bench.
4.2 ABLATION STUDY
We analyze method design and data properties. The training settings detailed in Sec. 4.1 We apply mPLUG-OWI2-7B (Ye et al. (0244) as the baseline all experiments (except in Tab.
Box Optimization. We evaluate box optimization in the annotation pipeline, including the box refinement (IQA filter and box merge) and the coordinate representation. We compare the models trained on GIQA-DES with (Ref-Box) and without refinement (Raw-Box: BBx) in Tab. 2a The refinement enhances the fine-tuning effect. We also visualize box area distribution in Fig. 6 Refinement reduces the difference between automatically annotated GIQA-160X and human-annotated GIQA-Bench. Besides, more analyses are provided in the supplementary material.
Meanwhile, compare discrete (Disc-Coord) and normalized continuous (Norm-Coord) box rep- resentations in Tab. 2b Results indicate that Disc-Coord enhances description quality (BLEU@4 and LLM-Score and grounding accuracy (Tag- Recalle,,ll,, compared with Norm-Coord
Multi-Task Training, We conduct an abla- tion on multi-task (GIQA-DES and GIQA- VQA) joint training. The results are listed Tab. 3 We observe that only GIQA-DES (Only-DESS can improve the quality assess- ment and grounding. GIQA-VQA improves VQA accuracy but exhibits limited ground- ing ability, likely due to reduced contextual information compared GIQA-DES. More- over, multi-task training (GIQA-160K) en- hances performance on both GIQA-DES and GIQA-VQA. It demonstrates the importance of data diversity.
Table 4: Ablation study on different baselines.
GIQA-DES
GIQA-VQA
Method
Tag Recall LLM-Score
Tug Recall Ace (Total)
LLaVA-1.5-7R
N/A
47.00
N/A
0.4733
0.5283
60.00
0,5961
0.6850
LLaVA-1.5-13B
N/A
49.00
N/A
0.4433
0.5548
60.50
0.7564
0.6950
LLaVA-1.6-7B
N/A
50.50
N/A
0.5067
0.5981
60.00
0.6538
0.7250
Owl.2-7B
N/A
48.25
N/A
0,5633
0.5474
63.00
0.7372
0.7417
Data Compatibility. We fine-tune various baselines using the proposed GIQA-160K. The results
are provided in Tab. 4 The results indicate that our proposed dataset is compatible with various MLLMs, effectively enhancing the grounding- ability of the model. Furthermore, we provide more detailed comparisons with more methods in Sec. 4.3
4.3 RESULTS ON GIQA-BENCH
In GIQA-Bench, we compare four groups of MI.MMs with different functionalities, iee. (1) General
models (General): LLaVA-v1.5-. 15-BB (Liu al. 2024a).
11---33BBBBBB (0.244 LA-
v1.6-BB (Liu et al. 2024b), and mPLUG-OW 72 (Ye et al. 2024); (2) Multimodal referring and
grounding models (Ground): Shikra-7B CChn et al. 2023). ), Kosmos-2-1.66 (Peng et al.) 2024). Ferret-7B You et al. 2024a), and GroundingGPT-77 (Li et al. 2024 (3) IQA models (QQA):
DepictQA-Wild-We
YYou et al. 2024b) and Instruct (Wu et al. 2024b) (fine-tuned three base
models); and (4) Our methods (Ours): Four general models fine-tuned on GIQA-160K. The detailed test settings and analyses are provided in the supplementary material.
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GIQA-DES Tag-
GIQA. VQA
Group
Method
miooi
BLEU8
LLM--Sore
mloU
Tag-Recall
Acc
(Tonal)
LLAVA-w1.--BB
2.82
47.00
NIA
0.4444
0.4733
LLaVA-v15-138
NIA
NIA
3.00
49.00
NIA
NIA
0.3888
0,5250
0.4433
General
LLVVA-116-7B
NIA
NIA
3.01
50.50
NIA
NIA
0,4559
0.5333
0.5067
mPLUGGOw/1277
NIA
3.62
0.5889
Shikra-778
0:4506
D.4768
0.40
27.00
0.4126
0.4359
0.5333
0.3917
0.4767
Ground
Kosmos-2-1.68
0.4946
0.3448
2.63
39.25
0.4982
0.4103
0.3889
0.4750
0.4233
Ferret788
0,6458
0.6778
3,16
43.75
0.5393
0.5769
0.4111
0,4875
0.4417
GroundingGPT-7B
0:4967
32.50
0.5321
0.5444
-iillQQA-Wii--BB
3.34
56.50
0.4333
0.5458
0,4783
IQA
Q-Instruct (LILaAA.V..5-13B)
O-Iastruct (LLaVA-v1.577B)
NIA
NIA
N/A
N/A
22.69
19.01
57.25
58.25
NIA
NIA
NIA
NIA
0.6222
0.6444
0.5417
0.5375
0.5900
0.6017
Q-lsstruct (mIPLUG-OWID277)
21.46
0.5375
0.5817
Grounding--C (1..VA-vv1.5 (-)
0.5763
D.52X9
19.02
60.00
D.5180
0.5961
0.7777
0.5458
0.6850
Ouns
Groundin-JQAA (LLaVA><1.5-133)
Grounding 10A (LLaVA-w1.6-7B)
0,6583
0.6302
0.5981
0.5518
20.24
19.17
60.00
60.50
0,5459
0.6830
0.6538
0.7564
0.8333
0.7889
0.5625
0.5512
0.7250
0.6950
Grounding 1QA (mPPLUU--OW1227B)
0.5955
22.87
0.6031
0.8141
Table 5: Quantitative results on GIQA-Bench. Best and second-best results are colored red and blue.
Describe evaluste the quallyy of the image.
The overall clarity image acceptable, with sufficient lightigg The texture details of the main subject the Enocchizoogg are rich and cear. he colors are alse rich. Howover, the compositon not good, as is slighty tiled. The textue dctails of the groint are also nch. Therefore, the image quality of this photo good.
What stands out image?
The striknng silver and red builet train
The image's quality affected by the weather conditions, mat sufface creating somewhat distorted ess features builking. are shill okcomibie, presence pottod EE0 scene adds of grocnery and to otherwise somber atmosphere created by the rain.
The train.
view gmiting Howeyer, is rootliee ander
T image features a traiz which is painted in sivee and red. It located at train Strtor and & parked on the trace
The overall clarity of this image is acceptable. The main subject, the buildin, reraiss most is texture detais. The background trees and ground are siigiiil burred, and there is some noise present. Therefare, the quality of this image is considered average.
The quality of the image not perfert, there is notiraazle blr amound the adges, which may be due to the camera's locus or the weather cons.tons. However, the overall composilton of the scene and the presencce of the pagoda and trees make the image visually appealing and interesting.
The
Ferret
IPLUG Owl2
Q O-instrctt-n..
Ground IQA (ours)
Figure 7: Visual comparisons on GIQA-Bench. Our proposed grounding- IQA (blue module) en- ables more fine-grained quality descriptions (left instance) and QA (right instance).
Quantitative Results, We evaluate all models on GIQA-DES and GIQA VQA from two aspects: quality assessment and grounding ability, as in Tab | General models perform poorly on both tasks, while task-specift models are more effective in their respective domains. Specifically, grounding MLLMs excel in grounding tasks but underperform on quality- objects/areas (GIQA- -VQA, Tag-Recall). Conversely, IQA models achieve high description quality (GIQA-DES, LLM-Score), but exhibit low accuracy in GIQA-VQA. In contrast, our method outperforms existing MLLMS.
Moreover, to further demonstrate the performance and generalization ability of our approach, we conduct extensive experiments and evaluations in the supplementary material, including: (1) tra- ditional score-based IQA tasks; (2) the user study on GIQA-Benehs and (3) the application of grounding-IQA to downstream tasks. Our method also achieves impressive performance.
Qualitative Results.
We provide some visual comparisons in Fig. 7 For GIQA-DES (left instance), the quality descriptions generated by general (mPLUG-OW12-7B Ye et al. 2024 and grounding (Ferret (You et al. 2024a)) MLLMs unsatisfactory. In contrast, quality more properly with coordinates of key objects affecting the quality. Furthermore, in the GIQA-VQA task (right instance), our method produces more accurate responses to image quality VQA involving spatial perception. More results are provided in the supplementary material.
method describes image
CONCLUSION
In this paper, we introduce a new IQA task paradigm called Grounding-IQA for fine-grained quality assessments. The grounding-IQ4 combines multimodal referring and grounding with IQA, and comprises two subtasks: GIQA-DES and GIQA-VQA. Under the task paradigm, we construct a corresponding dataset, GIOK- by an automated annotation pipeline. Meanwhile, develop a benchmark, GIQA -ennch,ecc, to evaluate the grounding IQA. Experiments indicate that our proposed task, dataset, and benchmark facilitate more tne-nndined IQA applications.

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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (62501386, 625B2116, U2541205, 62271414, 62572317), CCF-Tencent Rhino-Bird Open Research Fund, National Key R&D Program of China (2024YFF0505603), "Pioneer" and "Leading Goose" R&D Program of Zhejiang (Grant 2024SDXHDX0006, 2024C03182), the Key Project of Westlake Institute for Optoelectronics (grant number 2023GD007), and the 2023 International Sci-tech Cooperation Projects under the purview of the "Innovation Yongjiang 2035" Key R&D Program (grant num- ber 20247.126..
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Reviews:
[{"review_id": "LQtuGd2p1P", "reviewer": "Reviewer_U2TN", "summary": "The paper introduces Grounding-IQA, a novel Image Quality Assessment (IQA) approach tailored for multimodal large model learning (MLLM). The framework decomposes IQA into two subtasks: (1) GIQA-DES, which performs fine-grained, subject-level quality evaluation via grounding-based descriptions, and (2) GIQA-VQA, which assesses low-level attributes at localized regions using a visual question answering paradigm. The authors further present GIQA-160K, a large-scale dataset constructed with an automated annotation pipeline, and GIQA-Bench, an evaluation benchmark that jointly measures description quality, VQA accuracy, and grounding precision.", "questions": "1.Dataset Construction Details：The paper mentions that GIQA-160K was built through an “automated annotation pipeline.” Could the authors elaborate on how annotation quality and consistency were ensured? Was there any human verification or filtering process?How are noisy or ambiguous quality descriptions handled? Are there examples of failure cases in the annotation process?\n2.Whether human evaluation was included to validate these metrics.Without this information, it’s difficult to judge how faithfully GIQA-Bench reflects perceptual IQA quality.\nThe framework decomposes into GIQA-DES and GIQA-VQA. How much does each component individually contribute to overall IQA performance? An ablation study showing the relative importance of each subtask (and their potential synergy) would clarify whether the decomposition is truly beneficial or merely conceptual.\n4.It would be constructive if the authors could discuss potential limitations — for instance, the reliance on large-scale multimodal models that are computationally expensive to train and deploy, or the dataset’s possible bias toward certain types of degradation. A discussion on how future work might reduce these dependencies or enhance scalability would improve the paper’s outlook.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The manuscript is clearly organized, with a strong logical flow and well-designed figures and tables that effectively illustrate the framework and experimental setup. The writing quality is generally high, facilitating easy comprehension of complex ideas. Furthermore, the benchmark construction is comprehensive and well-motivated — evaluating models from multiple dimensions (descriptive, interrogative, and spatial grounding) offers a holistic view of multimodal IQA performance.", "weaknesses": "While the contribution of a large-scale dataset and benchmark is substantial, the main novelty resides primarily in dataset creation and annotation procedures rather than in methodological innovation. The proposed framework mainly adapts existing MLLM capabilities and fine-tuning strategies to the IQA domain. As such, the work may align more closely with a dataset or benchmark track rather than the ICLR main track, where new learning methodologies or theoretical insights are typically emphasized.\nAdditionally, there are a few minor grammatical issues throughout the text—for example, “provide” should be “provides” (line 53), and “new coordinates is” should be “new coordinates are” (line 268).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kg6sJ8ux8O", "reviewer": "Reviewer_WkTJ", "summary": "The paper proposes Grounding-IQA, a new paradigm for Image Quality Assessment (IQA) that couples spatial grounding with quality reasoning so models can locate and describe the local regions responsible for perceived degradations. It introduces two tasks: GIQA-DES (quality description with precise locations) and GIQA-VQA (region-aware quality QA where questions/answers include coordinates). To train and evaluate, the authors build GIQA-160K via an automatic pipeline and a 100-image evaluation set GIQA-Bench that scores description quality, VQA accuracy, and localization. Fine-tuning several mainstream MLLMs on GIQA-160K yields consistent gains on GIQA-Bench over generic MLLMs, pure grounding models, and pure IQA models, indicating better fine-grained perceptual quality understanding.", "questions": "See weakness.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. The paper integrates grounding with IQA to localize the regions causing quality defects—making it highly actionable for editing and restoration.\n2. Its end-to-end labeling/training pipeline—detection, IQA filtering, box merging, and coordinate–text fusion—is scalable and reproducible.\n3. A large training corpus and a targeted benchmark with multi-faceted metrics (generation, QA, localization) enable comprehensive evaluation.\n4. Consistent gains across multiple MLLM backbones, with ablations indicating the effectiveness of multi-task training and box-handling strategies.", "weaknesses": "1. Fix typographical errors in the references.\n2. Some novel IQA methods like (VisualQuality-R1) should be included.\n3. Conduct user studies to evaluate how well the output descriptions align with the corresponding images.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mldjQk9h1P", "reviewer": "Reviewer_hAUf", "summary": "This paper proposes a new IQA paradigm named Grounding-IQA. By combining multimodal referring/grounding with IQA, the framework achieves more fine-grained image quality evaluation. The authors design two tasks, GIQA-DES and GIQA-VQA, and construct an automated annotation pipeline to build a dataset named GIQA-160K. The authors also propose GIQA-Bench for performance evaluation. Both quantitative and qualitative results demonstrate the effectiveness of the proposed Grounding-IQA.", "questions": "1. Explain the considerations of specific parameter settings in the pipeline.\n2. Evaluate LLM-Score and Acc (W) using Llama-4 and analyze the results.\n3. Conduct ablation studies on the dataset (GIQA-160K) size.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Introducing multimodal grounding into IQA is a reasonable idea that aligns with human assessment logic. It also improves interpretability. \n2. The dataset GIQA-160K and the benchmark GIQA-Bench are well-designed contributions to the community. The automated pipeline is well-structured, which supports further research.\n3. Both quantitative and qualitative results validate the effectiveness of the proposed Grounding-IQA. Comparisons with various MLLMs (general, grounding, IQA) further confirm its advantages.\n4. Additional experiments in the supplementary material, including traditional score-based IQA tasks, user studies, and downstream tasks, provide further evidence of effectiveness.\n5. Ablation studies and visualizations support the validity of the designed pipeline and dataset.\n6. The paper is well written, with extensive figures, tables, and pseudocode that facilitate understanding.\n7. The supplementary material includes detailed implementation information, visualizations, and analytical results, making the content comprehensive.", "weaknesses": "1. Although implementation details of the pipeline are provided, some settings lack explanation or analysis. For example, why the discrete coordinates are set to 20×20, and why the number of tasks differs in GIQA-Bench?\n2. The work applies Llama-3 for evaluation. Considering the existence of more advanced models such as Llama-4, using them could provide more accurate evaluation results.\n3. The impact of dataset scale on performance is not investigated. It is not certain whether 160K data is necessary.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1dNiHUmnEk", "reviewer": "Reviewer_9RGv", "summary": "This paper introduces grounding-IQA, a new task paradigm that integrates multimodal referring and grounding capabilities with image quality assessment (IQA) to enable fine-grained quality perception. The authors identify that existing MLLM-based IQA methods primarily rely on general contextual descriptions, limiting their ability to perform detailed quality assessment. To address this, grounding-IQA consists of two subtasks: GIQA-DES (grounding-IQA-description), which provides detailed quality descriptions with precise spatial locations such as bounding boxes, and GIQA-VQA (visual question answering), which focuses on quality-related questions for local image regions. The authors contribute GIQA-160K, a dataset constructed through an automated annotation pipeline, and GIQA-Bench, a comprehensive benchmark that evaluates model performance from three perspectives: description quality, VQA accuracy, and grounding precision. Experimental results demonstrate that the proposed task paradigm, dataset, and benchmark effectively facilitate more fine-grained IQA applications.", "questions": "1. The automated annotation process relies on an existing MLLM (Q-Instruct) to label blur regions, which raises concerns about robustness and error propagation. Can the authors provide evidence demonstrating the reliability of this approach? A potentially more robust alternative would be to synthesize the dataset using pristine images and applying controlled blur degradations at different levels, which would provide natural bounding box annotations and standardized quality labels. How does the current approach compare to such synthetic data generation methods?\n\n2. The paper lacks detailed description of how data contamination between GIQA-160K and GIQA-Bench is prevented. Can the authors explicitly explain the data split strategy and provide evidence (e.g., image similarity checks, source verification) to ensure that the benchmark is completely disjoint from the training data?\n\n3. Given that models like Q-Instruct already possess strong IQA capabilities, wouldn't it be more efficient to enhance their grounding abilities rather than training a new model from scratch? Can the authors justify this design choice and discuss whether a fine-tuning or adapter-based approach on existing IQA-capable MLLMs might achieve comparable or better results with fewer resources?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.  The paper is well-organized with a clear logical flow, making it easy for readers to understand the motivation, methodology, and contributions. The task paradigm is clearly defined with concrete subtasks (GIQA-DES and GIQA-VQA), facilitating comprehension of the proposed approach.\n\n2.  The proposed automated annotation pipeline for constructing GIQA-160K represents a valuable contribution to the field. This approach addresses the scalability challenge of obtaining fine-grained quality annotations with spatial grounding, potentially benefiting future research in grounded IQA tasks.\n\n3. GIQA-Bench provides a well-designed evaluation framework that assesses model performance from three complementary perspectives: description quality, VQA accuracy, and grounding precision. This multi-faceted evaluation approach enables thorough analysis of grounding-IQA capabilities and sets a solid foundation for future work in this area.", "weaknesses": "1. Questionable Task Scope and Definition: The paper claims to address image quality assessment (IQA), which is a broad concept encompassing various quality dimensions such as saturation, color distortion, noise, compression artifacts, etc. However, the proposed method primarily focuses on motion blur of objects, which requires enhanced referring and grounding capabilities. This narrow focus does not adequately represent the full spectrum of IQA. The work would be more convincing if the problem were explicitly defined as \"motion blur assessment\" or \"local distortion grounding\" rather than claiming to solve general fine-grained IQA. The current framing creates a mismatch between the stated goal and actual contribution.\n\n2. Limited Generalization Evidence: Even for motion blur assessment, the method's scope appears limited. Most examples showcase salient objects (e.g., people, animals, vehicles), but it remains unclear whether the approach generalizes to other scenarios such as text blur, background element blur, or subtle quality degradations in non-salient regions. The proposed benchmark does not sufficiently demonstrate the model's generalization capability across diverse object categories and blur scenarios, raising concerns about practical applicability.\n\n3. Lack of Evaluation on Public Benchmarks: A critical experimental gap is the absence of evaluation on established public IQA benchmarks. The authors only test their trained model on the self-proposed GIQA-Bench, which limits the ability to assess the method's performance relative to existing approaches and raises questions about potential dataset-specific overfitting. Evaluation on standard IQA datasets would provide stronger evidence of the method's effectiveness and facilitate fair comparison with prior work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)