Do You Keep an Eye on What I Ask? Mitigating Multimodal Hallucination via Attention-Guided Ensemble Decoding
Authors: Yeongjae Cho
Keonwoo Kim
Taebaek Hwang
Sungzoon Cho
Venue: ICLR 2025 Poster (2025)
Keywords: Hallucination
Multimodal Hallucination
Large Vision-Language Model
Primary area: generative models
TL;DR: We introduce Ensemble Decoding (ED), a method designed to mitigate object hallucination in Large Vision-Language Models by dividing an image into sub-images and combining logit distributions with attention-guided weights to improve accuracy.

Abstract: Recent advancements in Large Vision-Language Models (LVLMs) have significantly expanded their utility in tasks like image captioning and visual question answering. However, they still struggle with object hallucination, where models generate descriptions that inaccurately reflect the visual content by including nonexistent objects or misrepresenting existing ones. While previous methods, such as data augmentation and training-free approaches, strive to tackle this issue, they still encounter scalability challenges and often depend on additional external modules. In this work, we propose Ensemble Decoding (ED), a novel strategy that splits the input image into sub-images and combines logit distributions by assigning weights through the attention map. Furthermore, we introduce ED adaptive plausibility constraint to calibrate logit distribution and FastED, a variant designed for speed-critical applications. Extensive experiments across hallucination benchmarks demonstrate that our proposed method achieves state-of-the-art performance, validating the effectiveness of our approach.

Published as a conference paper at ICLR 2025
Do YOU KEEP AN EYE ON WHAT I ASK? MITIGATING MULTIMODAL HALLUCINATION VIA ATTENTION-GUDDER ENSEMBLE DECODING
Yeongjac Cho 19 Keonwoo Kim 2", Tacback Hwang 3, Sungzoon Cho 1f 'Scoul National University. "Kim Chang AI&IT System Center, 'adddee Corporation yj yjcho@bdai. enu.ac.a kr, gunny12540gmaliu com taebaek@waddlelabb com, zoon@anu.. ac.kr
ABSTRACT
Recent advancements Large Vision-Language Models (LVLMs) have signifi- cantly expanded their utility in tasks like image captioning and visual question answering. However, they still struggle with object hallucination, where mod- els generate descriptions that inaccurately reflect the visual content by including nonexistent objects or misrepresenting existing ones. While previous methods, such as data augmentation and training-free approaches, strive tackle this issue, they still encounter scalability challenges and often depend on additional exter- nal modules. In this work, we propose Ensemble Decoding (ED), novel strat- egy that splits the input image into sub-images and combines logit distributions by assigning weights through the attention map. Furthermore, we introduce ED adaptive plausibility constraint to calibrate logit distribution and FastED, variant designed for speed-critical applications. Extensive experiments across hallucina- tion benchmarks demonstrate that our proposed method achieves state-of-the-art performance, validating the effectiveness of our approach.
INTRODUCTION
Recent advancements in Large Language Models (LLMs) (Brown) 2020 TTovron et al. 2023a b
Is there a mouse the image?
Jiang et al., 2023) have extended their capabilities into the visual domain. In particular, Large Vision-
Language Models (LVLMs) (Liu et al. 2023b Bai
Crop
al. 2023 Liu et al.) 2024b Dai et al. [023] Gong et al.
2023) process visual inputs and generate con-
Resize
textually relevant text, making them effective for tasks such as image captioning and visual question answering. Despite extensive research focusing on optimizing LVLM architectures, training paradigms, and dataset combinations, the persistent issue of ob- ject hallucination raises significant concerns about the reliability and applicability of these models (Liu) et al. 2023a Lovenia et al.] 2023 Li et al. [2023b] Liu et al.] (0224)). Object hallucination occurs when LVLMs maccurately describe visual content, mis- representing or introducing nonexistent objects.
No
Yes
LVLM
LVLM
Figure Example of object hallucination in LVLM (left). After cropping and resizing the image, the model answers correctly (right).
Object hallucination is especially problematic in applications that demand precise answers, such as autonomous vehicles (Iberraken & Adouane (2023) and manufacturing systems (Mohammadi Amin et al., 2020). To address the issue, researchers have proposed strategies such data augmenta- tion and fine-tuning with specific datasets (Rohrbach et al., 2018 Gunjal et al. 2024), but these
"Equal contribution. Corresponding author.

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approaches often struggle with scalability and generalization. Recently, training- ffree methods have emerged, including contrasting logit differences across layers (Chuang et al.) (023). retrospection- reallocation using logit penalties (Huang et al. 2024), contrastive decoding with noise (Leng al.) 2024), and the combination of local and global attention (An et al., 2024). While these methods show substantial improvement, they often fail to exploit intrinsic visual information fully.
As illustrated in Figure | we observe that inputting a relevant sub-image, a portion of the original image, into the model improves the performance of the generated response. Motivated by this obser- vation, we conduct a pilot study to analyze how visual characteristics influence the model's visual representation. Our analysis identifies two key factors that can negatively affect model performance: (1) unnecessary objects in the image, and (2) low object resolution within the image. First, images containing numerous irrelevant objects to the question disrupt the model's focus, leading to object hallucinations. Second, when the object resolution in the image is low, it impairs the model's ability to interpret visual content accurately, resulting in incorrect inferences. This analysis suggests that in- stead of spreading focus across all areas, concentrating selected sub-images enhances the model's ability to mitigate object ballucinations by effectively harnessing intrinsic visual information.
Motivated by these findings, we introduce Ensemble Decoding (ED), novel training-free decoding strategy for LVLMs to mitigate object hallucination. ED splits the input image into sub-images by dividing the original image into several parts. It then aggregates logit distributions of each sub- image, which contain fewer objects and a higher resolution per object, along with those of the original image. ED leverages attention maps to prioritize sub-images, dynamically assigning dif- ferent weights at each step of token generation to emphasize the necessary parts at that specific moment. This approach addresses the limitations of existing methods by adaptively exploiting in- trinsic visual information without relying on additional modules. Furthermore, to consolidate the ensembled logits from sub-images more effectively, we introduce the ED adaptive plausibility con- straint, which calibrates logit distributions to ensure fine- aie-grained tokens contribute to the output. Additionally, since processing multiple sub-images increases the computational cost of the ED pro- cess, we develop FastED, an optimized variant that balances performance and speed by referencing only the sub-image with the highest mean attention score. Through extensive experiments on ob- ject hallucination benchmarks, we demonstrate that our proposed method achieves state-of-the-ar results across most benchmark evaluation metrics, outperforming existing methods and confirming the effectiveness of our approach. The contributions of this paper are fourfold:
We propose ED, a training- free decoding strategy for LVL. that mitigates object halluci- nation by leveraging attention maps split the input image into sub-images and combining their logit distributions.
We introduce the ED adaptive plausibility constraint, which calibrates logits across multiple images to ensure fine-grained tokens contribute to the output.
We develop FastED, an optimized variant of ED, balancing performance with speed by selecting a sub-image with the highest mean attention score from the original image.
Through extensive experiments, we demonstrate that ED achieves state-of of-the-art results across most benchmark evaluation metrics, outperforming existing methods.
2
PILOT STUDY
In this study, we investigate whether properly divided sub-images can reduce object hallucination in the outputs of LVLMs. To assess whether the LVLMs focus on the object relevant to the query while generating tokens, employ attention maps. Cha et al. (2024) highlights that patch-wise projectors (Liu et al. 2023b Chen et al.) 2023a) preserve spatial locality, enabling more precise attention maps compared with resampler-based models (Bai et al. 2023 Dai et al. 2023 Ye al.
2023 Zhu et al. 2023). Therefore, we adopt watch-wise projectors, specifically LLaVA-I.5 (Liu al. 2024b), as the base model. We hypothesize that irrelevant objects and low object resolution in images are likely to impact performance negatively. To validate this hypothesis, we conduct experiments using grid-arranged images with randomly placed objects, accompanied by questions about specific objects in the image. Our objective is to verify if the model's highest mean attention score consistently aligns with the correct grid cell before generating an answer.
2

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In experimental setup, manipulate two vari- ables: (1) masking 1/4 of the unnecessary area to re- duce irrelevant objects and (2) lowering the object res-
Maslang
Normal
Low- Resolution
100
olution in the image, in order to assess their impact on model performance. As shown in Figure 2 apply- ing masking to unnecessary objects improves perfor- mance, while lowering the resolution of objects leads to decline. This trend becomes more pronounced as the number of objects increases. As sub-images gen- erally contain fewer unnecessary objects and maintain a relatively higher resolution for each object compared to the original images, our findings confirm that sub- images help the model better attend to relevant ob- jects. This is crucial for mitigating object hallucina- tions and improving overall performance (Yang et al.) 2021), thus supporting the use of sub-images in our method. Further details are available in Appendix
Accuracy
70
4
16
36
64
Number of Objects
Figure 2: Experimental results of the pi- lot study. Masking refers to masking some irrelevant objects in the image, while Low- Resolution involves reducing the resolution of each object in the image.
METHOD
Overview We present the input structure and notation for our proposed Ensemble Decoding (ED), as illustrated Figure 3 In this approach, raw input images are split into multiple sub-images, which, along with the original raw image, are fed into a pre-trained LVLM, denoted as pe parame- terized by 6. Given a raw input image v, we split it into N sub-images, each of size X c, denoted as U1, ........ E Re The original image v and the N sub-images, along with the text 2, are separately fed into the LVLM,, resulting total of N + image inputs.
3.1 ENSEMBLE DECODING
Attention-Guided Weight In Ensemble Decoding, we calculate cncention-guidde weights for mul- tiple sub-images derived from the raw input image at each decoding step t. The following equations detail the steps involved in the process. First of all, compute the attention matrix A at time step t using the query Q2 and key K, matrices from the self-attention mechanism (Vaswani 2017), which involves the text x. the image v, and the previously generated tokens yet:
(QcK?" Vdz.
A; = softmax
(1)
where de denotes the dimension of the key vectors. Following similar approach to Lee al. (2023) we select the top K layers with the highest mean attention scores and average them to form single representative layer to improve the attention matrix. Within the multi-head attention mechanism, we identify the top H heads with the highest mean attention scores and average them to obtain a single attention matrix. The resulting attention matrix A, is reshaped into a matrix of size d x d:
K
Å, = H
Σ sorted j=l
1
Σ sorted
[1] E Rdxd
(2)
K
where d x d denotes the number of patches. Subsequently, we aggregate the refined attention matrix corresponding to each of the N sub-images. Specifically, we identify the regions in A, that corre- spond to each sub-image and sum the attention values within these regions to obtain the aggregated attention scores 8k,t where l indicates the index of each sub-image:
Sk.6
A for k= 1=1,2..... N.
(3)
(egg)
3

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Is there spoon the mmage?
I.VLM
Logit Ensemble
0.01
0.03
13
0.03
0:92
Engilaly jr Ycc)
Attentlon-Gulded Weight
Yeer
No
fu
a) logito(y Jv, x.a
LVLM
E Iv y.yr) -ere-a---- -ax
1/
12
Toi
20
N0
No
Adaptive Plansibility Constrainl
10
26
20
26
Σ, xog
logit (.
Figure 3: Overall pipeline of Ensemble Decoding (ED). Attention-guidel weights applied to sub-images and combined with the logits from the original image for ensembling. ED adaptive plausibility constraint is applied generate the final output. The entire process is dynamically repeated at each time step t of token generation.
Finally, we convert the aggregated attention score into attention-guided weight by applying soft- max function with temperature T.
SELt exp(sk.e/T)
(4)
2Ø=1 exp($),//T)
This assigns an attention-guiin weight fa. to each of the sub-images each decoding step t, indicating its relative importance and ensuring that each sub-image contributes to the final output based on its significance.
Logit Ensemble Each sub-image Uk is processed along with the associated text and the previ- ously generated tokens V<* to produce a logit distribution logit, T, Dee), where yr denotes the token at decoding step These logits are weighted by their corresponding attention-guided weight fa.e. The final logit distribution is computed by combining the weighted sum of the sub- image logits with the logit distribution from the raw image v, using weighted
[0.
(V V, Mee) = softmax [(1 - (o)itt, u. Ver) + Σ logit, Uk x. Vee) fa,
(5)
where V denotes v, VINN the set containing the raw image and sub-images. This approach uses both the global context of the raw image and the local context of the sub-images at each decoding step
3.2 ED ADAPTIVE PLAUSIBILITY CONSTRAINT
In previous studies, adaptive plausibility constraint has been utilized in single-image scenarios to preserve tokens with high original probabilities and discard less likely ones (Li et al., 2022 Leng et al. 2024 An et al. 2024). However, in ED, integrating probabilities from multiple images yields more detailed and fine- -raiind representations that must be preserved to ensure accurate analysis. To address this, we introduce the ED adaptive plausibility constraint. It adjusts the truncation strength through a hyperparameter 8, which is applied to the weighted sum of probabilities calculated from each of the N sub-images. The constraint is defined follows:
)} (6)
Vneaa (yee)= = 1/2 V Σ (o Uke x. vee) 2 33 max Σ Po Uky x, Vee) fu.e) (mx))
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The probability for tokens not in Phead(Ue)) is set to zero:
PED V.z) O, if Yo Vyead(y<ce..
ensures that only the most plausible tokens based on the weighted sum of logits across all sub- images contribute the final probability distribution, preserving the integrity of HD.
3.3 FASTED
While ED enhances model performance by capturing finer details through multiple sub-images, increases computational cost due to multiple forward passes. To balance performance and speed, we introduce FastED, which only uses the raw image and the sub-image with the highest attention- guided weight to compute the logit distribution. It significantly reduces the computation from N + to 2 forward passes, with minimal performance trade-off, as demonstrated in our experimental section. The modified equation for FastED as follows:
PPastED(1 Uk*, v, 8-1) softmax [[1 a (ce) logit, (yt uk v, Mct))
where k* corresponds to the sub-image with the highest attention-guided weight fi...
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Dataset and Evaluation Criteria We evaluate ED on the POPE and CHAIR benchmarks, which focus on object existence hallucination, and further assess its performance on the MME and LLaVA- Bench, which evaluate additional attributes beyond object existence. Detailed descriptions of each dataset are provided below.
POPE (Polling-base based Object Probing Evaluation) (Li et al. 2023b) includes 27,000 Yes/No ques- tions across three datasets (MSCOCÓ, A-OKVQA, GQA) (Lin et al. 2014 [Schwenk et al. [022] Hudson & Manning] 2019), balanced equally between existent and non-existent objects. Non- existent samples are constructed using random, popular, and adversarial settings. We use precision, recall, Fl score, and accuracy as evaluation metrics.
CHAIR (Caption Hallucination Assessment with Image Relevance) (Rohrbach et al. 2018) mea- sures object ballucination in image captions by calculating the proportion of objects mentioned that do not appear in the ground-truth labels. Following An et al. (202)). we randomly select images from the MSCOCO (Lin et al. 2014) and use CHAIRs, CHAIRi, and recall evaluation metrics.
MME (Fu et al. 2024) provides a benchmark for assessing LVLMs across various tasks. Following evaluate hallucination using four subtasks: Existence, Count, Position, and Color, with performance measured by the combined metric of accuracy and accuracy+.
Yin et al. (2023)
LLa LLa VA-Bench (Liu et al., 2023b) consists of 24 images and 60 questions to assess LVLMs' per- formance on complex tasks and domain adaptation. Following Leng et al. (2024), we use GPTT4 to assess the accuracy and detailedness LVLM's image captioning using a 10-point Likert scale.
Baselines As baselines, we use the commonly adopted multinomial sampling decoding method (Regular) and four other of-te-of-the-rr training-free decoding strategies for object hallucination tasks. VCD (Leng et al., 2024) reduces hallucinations through contrastive decoding using noisy images. DoL.A (Chuang et al. 2023) suppresses hallucinations by contrasting logits from the first and last layers of the model. OPERA (Huang et al., 2024) mitigates hallucinations by penalizing certain knowledge aggregation patterns. AGLA (An et al. 20024) tackles attention deficiency issues by integrating global attention with local attention derived from masked images.
Implementation Details In all experiments applying ED, we set N = 4 to divide the input image into sub-images. The LVLM model used in all experiments is LLaVA-1.5 (Liu et al., 2024b). The
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Table Experimental results of POPE on different decoding strategies.
Setting
Decoding
Precision
Recall
Fl Score
Accuracy
Regular
88.84
76.76
82.28
83,49
DOLA
87.59
81.27
84.19
84.78
OPERA
94.52
79.80
86.45
87.53
Random
VCD
87.15
86.68
86.83
86.84
AGLA
94.41
82.08
87.71
88.54
ED
93.40
86.41
89.68
90.08
Regular
82.47
76.76
79.34
79.98
DOLA
84.11
76.22
80.61
79.75
OPERA
88.00
79.80
83.50
84.21
Popular
VCD
87.15
80.59
83.37
82.65
AGLA
87.88
82.08
84.68
85.14
ED
86.12
86.41
86.00
86.09
Regular
76.11
76.80
76.26
76.03
DOLA
77.27
75.47
76.16
76.32
OPERA
82.16
79.76
80.69
80.88
Adversarial
VCD
73.43
86.47
79.28
77.31
AGLA
81.20
82.10
81.36
81.13
ED
79.75
86.47
81.90
82.75
Table 2: Experimental results subset of CHAIR with different decoding strategies. To ensure fair comparison, we include the average length of generated outputs, as CHAIR metrics and recall vary with different output lengths. Baseline results referenced from An al. (2024.
Decoding
CHAIRs
CHAIR4
Recall
Average Length
Regular
51.0
15.2
75.2
102.2
DOLA
57.0
15.9
78.2
97.5
OPERA
47.0
14.6
78.5
95.3
VCD
51.0
14.9
77.2
101.9
AGLA
43.0
14.1
78.9
98.8
ED
43.0
14.0
82.5
100.1
hyperparameters a and 3 are set to 0.5. The softmax temperature is set to le-2 for short-answer tasks like POPE and MME, and le-4 for longer-answer tasks like CHAIR and LLa The attention map generation follows (Lee et al. 20033, with H and K set to 3. We use an A6000 GPU, and experiments are repeated three times, with reported results averaged.
4.2 MAIN RESULTS
In the main experiment, we evaluate the performance of ED on the POPE and CHAIR benchmarks, which assess the existence of objects. Table preseens the results of POPE in the Random, Popular, and Adversarial settings, where ratio of Yes/No labels 50%. In terms F1 score and aceuracy, ED shows average improvement of 6.57%p and 6.47%p, respectively, compared to Regular, demonstrating the effectiivness of ED. Moreover, ED consistently outperforms previous state-of- the-art methods across all POPE settings.
Unlike POPE, which uses Yes/No labels to assess the presence of objects, Table presents the results for the CHAIR benchmark which specifically evaluates the presence of object hallucinations in detailed image captioning. this experiment, ED achieves the highest recall, indicating significant improvement in the detailedness of generated captions. Compared to Regular, ED shows substantial

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200
160
6.5
80
5.5
40
Accuracy
Detailedness
Regular
DOLA
OPERA
VCD
AGLA
m ED
Regular
AGLA
FastED
ED
Figure 4: Experimental results of MME hallu- cination subset with different decoding strategies.
Figure 5: Results of GPT-aided evaluation on the captions of
(Regular) The image presents pier extanding into mountain Lake, with simple boat dock next to it Along the pier, enjoy serene atmosphere. Tn the background, claudy sky adds depth to the scene, naking the lake the main focal point the image
can
(AGLA) The inage features piar extencing out into large body of water There are several people scattered along the pien, with standing closer the edge and others further back. A boat can be seen in the water near the pier, atnosphere..
adding
Snow Crow-Caaped Mountains
(ED) The inage features wooden pier extending out over a large bady of water, likely of lake or a river. nountain, providing picturesqué pier is empty, with people visible in the scene. in the background, there are few snow-capped mountains, adding to beauty of the scene. water appears caln and serene, reflecting tranquility of the environnent.
pier situated
surrounding landscase.
Figure 6: Generated captions using Regular, AGLA, and ED decoding strategies. Red text indicates hallucinations. Attention maps illustrate the state before generating each word.
performance improvements across all metries. Specifically, ED outperforms the previous state-of- the-art method, AGLA, with 3.6%p improvement in recall and 0.1%p reduction in CHAIR. Although ED performs equally to AGLA on CHAIRs|. these results confirm ED's effectiveness in reducing object hallucinations in generated captions.
DISCUSSION
5.1 BEYOND OBJECT HALLLCINATION
In this section, we extend our analysis to include hallucinations involving object attributes. As shown in Figure ED shows the highest performance on position and color-related questions and achieves near-perfect accuracy on existence- related questions on the hallucination subset of the MME dataset. However, struggles with object counting, likely due to the image-splitting process fragmenting ob- jects, which complicates quantity-related tasks. Moreover, level of captions generated on LLaVA- LLVA-BBnnhh using GPT-4. As detailed in Figure 5 both FastED and ED outperform Regular and the previous state-of-thh--rt model AGLA. ED, in particular, shows a significant improvement in detail compared to FastED. This performance boost likely due to referencing all split images, rather than only the with the highest mean attention score.
also evaluate the accuracy and detail

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leveraging the full image context, ED generate more detailed and comprehensive captions, ef- fectively capturing fine-gratt finegrrineed details while maintaining high accuracy across visual attributes.
5.2 QUALITATIVE ANALYSIS
To further assess whether ED effectively mitigates object hallucination beyond quantitative metrics, we conduct qualitative evaluations using other decoding methods as baselines. As shown in Figure both Regular and AGLA generate hallucinations (in red text). Specifically, key limitation of AGLA that it computes attention from the initial image and question only once, creating fixed masked image by masking low-attention areas. Since this masking is static and does not dynamically update as new tokens are generated, AGLA struggles to describe fine details. In contrast, ED generates different attention map at cach token generation step and performs the logit ensemble process continuously. It allows the model to adapt to the context and better identify relevant image regions, significantly reducing hallucinations. Figure/jillustrate ED's ability generate dynamic attention maps at each step (in blue text). Additional qualitative examples are provided in the Appendix E
5.3 COMPUTATIONAL EFFICIENCY
To evaluate the computational effi ciency and performance ED and FastED, we conduct experiments the CHAIR benchmark, comparing them with Regular and AGLA, shown in Table 3 AGLA is in- cluded it is the hest-performing existing method. While Regular of- fers the shortest inference time due to its lack of additional techniques, underperforms
Table 3: Inference latency and performance results. Latency refers to the average time per image for caption generation.
Decoding
Latency
CHAIRS
CHAIR
Recallt
Regular
5.25
51.0
15.2
75.2
AGLA
7.33
43.0
14.1
78.9
FastED
6.96
43.7
13.1
74.0
ED
16.42
43.0
14.0
82.5
object hallucination tasks. contrast, ED achieves the highest performance (82.5% in recall) but requires the longest inference time due to multiple forward passes. FastED addresses this by reducing inference time by more than half, making 2.36 times faster than ED and slightly faster than AGLA. FastED not only accelerates the process but also achieves signifi- cantly better CHAIR metric scores than Regular, striking balance between speed and performance. Although AGLA also mitigates object hallucination by removing unnecessary parts and assembling logits, relies on external modules, reducing efficiency and lacking dynamic adaptation, which leads to lower recall. Thus, ED is recommended when maximum performance and detailed results are crucial, while FastED is better suited for scenarios where inference speed a priority.
5.4 ABLATION STUDIES
Attention-Guided Weight To fur- ther validate the effectiveness of attention-guided weight, we assess the model's performance by assign- ing uniform weights to all sub- images instead utilizing attention- guided weights. As shown in Ta- ble [ excluding attention-guided weight results in significant perfor- mance drop. These findings under- score the critical role of attention- guided weight in enhancing the effectiveness of ensemble decoding.
Table 4: Results on the POPE MSCOCO benchmark com- paring uniform weight and attention-guide guided weight.
Setting
Weighting Strategy
Fl Score
Accuracy
Uniform
80.68
83.60
Random
Attention-Guided
88.84
89.63
Uniform
80.02
82,90
Popular
Attention-Guider
87.34
88.03
Uniform
78.44
81.23
Adversarial
Attention-Guided
84.59
84.97
ED Adaptive Plausibility Constraint We evaluate the effectiveness ED adaptive plausibility constraint in object hallucination tasks, comparing it to adaptive plausibility constraint (Li et al. 2022 Figure / shows the averaged accuracy for both methods on the POPE evaluation. With 0, both methods produce the same results, constraint is applied. However, as B increases,
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APC -4-ED RD APC
Regular (7R)
ED (7B)
Regulaar BD (133)
86
90
Avg. Ace
Avcurary
80
75
70
B
65
9ondom
Adsamoriol
Figure 7: Ablation studies on the general adap- tive plausibility constraint (APC) and ED adap- tive plausibility constraint (ED APC). Averaged POPE accuracy is reported, with performance evaluated by varying B.
Figure 8: Ablation studies on model size, showing comparisons between Regular and ED for the 7B and 13B parameters across different POPE settings.
the gap between the methods widens. While adaptive plausibility constraint outperforms the no- constraint case, it consistently underperforms ED adaptive plausibility constraint at all non-zero (3.
Different Model Sizes We perform an ablation study to evaluate the effectiveness of ED across different model sizes. Using Regular as the baseline, we measure accuracy on POPE with LLaVA- 1.5 models having 7B and 13B parameters. As shown in Figure g ED consistently outperforms Regular, regardless of model size, confirming that our method is not limited by model capacity. Ad- ditionally, the 13B ED model generally achieves higher performance than the 7B model, indicating that ED's effectiveness improves with larger models. These results demonstrate that approach scales well with model size while maintaining its core advantages.
RELATED WORKS
Large Vision-Languaa Models Recently, LVLMs (Liu et al. 2023b Bai ct al. 2023 Liu et al,, 2024b Dai et al. 2023 Cha et al.
2024 have emerged as pivotal innovations, combining natural language processing and computer vision to enable models to follow instructions based on visual inputs. Despite advancements in training pipelines (Chen et al. 2023b Liu al., (024b) and multi- task capabilities (Chen et al., 2023a Zhang et al., 2023b; Li et al. 2024), efficiently encoding and integrating visual information into L.Ms remains of the central challenges. LVLMS are broadly
categorized by the type of projector used: the patch-wise projector (Liu et al.] [2023b] 2024b) CCa
al. 2024; Chen et al.) 2023a) and the resampler (Bai et al. 2023 Dai et al.) 2023 Ye et al.)
2023 Zhu et al. 2023). The patch-wise projector preserves spatial feature locality but incurs higher computational costs at higher resolutions due to more visual patches (Cha al. 2024]. Conversely, the resampler reduces token numbers but struggles to retain visual feature locality. We focus on
the patch-wise projector method, which preserves the spatial locality of image patches, enabling effective use of attention maps.
Object Hallucination in LVLMs
Hallucination LLMs refers to the generation of nonsensical
nonexistent information (Ji al. 2023 Zhou al. 2020: Zhang et al.) 2023c Li et al.) 2023a
Zhang et al. 2023ab. In LVLMs, object hallucination involves incorrect descriptions of nonexistent
objects or misinterpretations of image content (Biten al. 2022. Rohrbach et al., 2018 Li al.,
2023b). Efforts to address object hallucinations have included utilizing preference optimization (Sun al. 2023 Gunjal et al. 2024; Sarkar al. 2024]] Chen et al. 2023c Ouali al. 2024) and post- hoc revisers (Zhou et al., 2023 Wu et al. 2024; Yin et al. (023). 3). but these require supplementary
data or training, which leads to increased computationa costs. To tackle these issues, various de-
coding strategies have been proposed. VCD (Leng et al. 2024) proposes contrastive decoding that mitigates hallucinations by adding noise to images to counteract inherent model biases. Other con- trastive decoding methods, such ICD (Wang et al. 2024) and IBD (Zhu et al.) 2024), have also been introduced. OPERA (Huang et al. 2024) analyzes hallucination patterns from knowledge ag- gregation and mitigates them by applying penalty terms to these patterns. AGLA (An et al.) 2024) addresses attention deficiency with an image- poonteepromiingg module that applies masks to extract local
features but lacks dynamic adjustment during token generation. HALC (Chen al. 2024] uses

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additional detector for grounding and adaptive focal contrast but is limited by its time- cone-connuming process and dependence on external modules. In contrast, our approach enhances the LVLM's at- tention mechanism with dynamic, mrodule-free method to more effectively mitigate hallucinations.
7 CONCLUSION
In this paper, we propose Ensemble Decoding (ED), novel strategy for mitigating object hallu- cination in LVLMs. By splitting input images into sub-images and leveraging attention maps, ED enhances logit distribution accuracy, effectively utilizing intrinsic visual information. Additionally, we introduce ED adaptive plausibility constraint to refine outputs, and FastED, a variant ED designed for speed-critical applications. Extensive experiments across various object hallucination benchmarks demonstrate that ED achieves state-of-the-art performance, validating its effectiveness.
Limitation While ED significantly improves performance, it currently applies only to LVLM ar- chitectures that preserve spatial locality and use patch-wise projectors, thus limiting its applicability across all model types. Additionally, despite ED's empirical success, we lack rigorous theoreti- cal proof explaining its effectiveness. Future work will address this, aiming to provide a stronger theoretical foundation and extend ED's s applicability to more architectures.
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A
PILOT STUDY: OBJECT-GRIN ATTENTION ALIGNMENT
Normal
Masking
Low-Resolution
Attention Map
(b)
(c)
(d)
Figure 9: Example of a 4 4 grid image used in the Object-Grid Attention Alignment experiment. (a) shows the normal image, (b) illustrates the image with non-relevant areas masked, and (c) represents the image with reduced object resolutions. (d) displays the attention map from the LVLM when given the question (Is there donut in the image?). The highest mean attention score corresponds to the position of the donut grid cell.
To better understand how visual characteristics influence visual representations, we conduct the Object-Grid Attention Alignment experiment, using attention maps to evaluate their impact. In this experiment, arrange various objects in a grid format to facilitate quantitative evaluation. The grid images are created using DomainNet dataset (Peng et al. (019)) a large-scale collection of common objects across six domains. It includes clipart, infograph, painting, quickdraw, real, and sketch domains, with 345 object categories such as bracelets, planes, birds, and cellos. For our experiment, we specifically use the real domain, which consists of real-world photographs. setup is shown in Figure aa). We vary the number of objects and grid sizes within the images, experimenting with 2 2, 4 x 4, 6 6, and x 8 grid configurations. For each grid size, we generate 100 randomly arranged images to ensure comprehensive evaluation of how different grid sizes and object counts impact model performance. These variations allow us to assess the model's attention alignment across range of visual complexities.
example of this
We evaluate the model's attention maps by observing the attention values corresponding to each object's position in the image. For each object, we calculate the mean attention value over its region as detailed in Equation 3 This approach allows us to evaluate the model's accuracy correctly identifying the object based on attention alignment. Additionally,
apply two image modifications for further evaluation: (1) Masking, where 1/4 of the random non-relevant regions of the image were removed, and (2) Resoluttnn where the resolution of objects in the image are intentionally reduced. These modifications are shown in Figure and Figure respectively, and they allow us analyze the impact of visual characteristies on how LVL.Ss understand the representation of images in response to queries and where they focus their attention.
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B DETAILED EXPERIMENTAL SETTINGS
Image Dimensions Distribution
Heegght Distribution
Width Distribution
890
600
Whath xexx
603
304
100
image
Images
Figure 10: Statistical boxplots representing the height and width distributions of MSCOCO (in
al. 2014) images used in the main experiment.
The number of sub-images In our experiments, set the sub-image size to 336 336 match the resolution for which CLIP-ViT-LE -IP-vii---336px (Radford et al.] 2021), the vision encoder used in LLa (Liu et al. 2024b), was pretrained and fine-tuned. Figure I0] illustrates the statistical distribution of the height and width of images used in the main experiment. The majority of images fall within the range of 400 600 pixels, indicating that four sub-images sufficient for effective representation. Consequently, we established N as 4 for this research.
Table 5: Results the POPE MSCOCO benchmark with and without overlap.
Setting
Method
Score
Accuracy
ED (w/o overlap)
84.68
86.27
Random
ED
88.84
89.63
ED (w/o overlap)
83.47
84.97
Popular
ED
87.34
88.03
ED (w/o overlap)
80.57
81.40
Adversarial
ED
84.59
84.97
Overlap between sub-images To ensure continuity across sub-images, we allow overlap when segmenting the original image. Specifically, for images with a width or height exceeding 672 pixels, we first resize them to 448 x 448 before applying our method, ensuring that adjacent sub-images share overlapping regions. This strategy helps mitigate issues where objects near the center of sub-images might otherwise be artificially split. To assess the effectiveness of this approach, compare our method with a non-overlapping baseline, where images are simply divided into four equal parts without any overlap. As shown in Table 5 incorporating overlap prevents objects from being fragmented across sub-images and leads to a significant improvement in performance.
Hy pirpaaarrsessrs in FastED In this variant, the temperature parameter is not used to streamline the computation. All other parameters are kept consistent with those used in ED.
CHAIR We use 100 selected images in MSCOCO (Lin et al. 2014) following the approach of An al. 2024 Then, we employ the prompt Please describe this image in detail generate detailed image captions. These captions are then evaluated for object hallucination. The equation used to evaluate CHAIR is presented as follows:
[Captions w/ hallucinated objects)
| HHallucinated objects}|
CHAIRs
CHAIR
(8)
|All captions)|
[All mentioned objects]
https://github.r hat.//aacke////m AGGLA
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1 |Accurate objects]|
Recall=
GGround-truth objects]]
GPT-aided Evaluation For LLaVA-Bench (Liu et al. 2023b) GPT-aided evaluation, we use the prompt Describe this photo in detail to generate detailed image captions following the methodology outlined in Yin et al. (2023); Li et al. (2022): : An et al. (2024). We employed the GPT-4c API to assess and compare the accuracy and detailedness of the captions.
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C
ABLATION STUDIES
C.I WEIGHTED TERM IN ED
Table Results on the POPE MSCOCO Random with varying a values and without ED APC.
a
Score
Accuracy
O (Normal Decoding)
79.67
81.40
0.3
80.27
81.80
0.5
80.92
82.22
0.7
81.66
82.73
We conduct additional experiments, specifically performing ablation studies scenarios where ED adaptive plausibility constraint is not used (8=0). The results, summarized in Table 6 show that increasing a consistently leads to improved performance. This suggests that giving more weight to sub-images positively impacts overall model performance.
C.2
EFFECT OF ON CAPTIONING ACCURACY AND DETAILEDNESS
Table Results of GPT-aided evaluation on the captions from LLaVA-Bench with varying 3 values.
B
Accuracy
Detailedness
0.01
6.14
5.92
0.1
6.72
6.50
0.5
6.93
6.46
To further explore the optimal value of B in ED, we conduct an experiment on image captioning using LLAVA-Bench, task that differs in focus from POPE. In this experiment, we vary the B while keeping other parameters constant. Along with the default setting 88005, we test additional values, such as 0.1 and 0.01. As shown in Table 7 higher 3 values consistently lead to improved performance in terms of accuracy. A similar trend observed for detailedness; however, when set to 0.01, the results noticeably lower compared to 8=0.1 and 8=0.5, which produce nearly identical outcomes. These findings suggest that while the optimal |3 value may vary across bench- marks, increasing 3 generally enhances performance, contributing more accurate and detailed captions for sub-images.
C.3 RESULTS OF MODEL SIZE ABLATION
Table 8: Quantitative results of POPE for different model sizes.
Setting
Model Size
Decoding Strategy
Precision
Recall
Fl Score
Accuracy
Regular
88.84
76.76
82.28
83.49
7B
ED
93,40
86.41
89.68
90.08
Random
Regular
88.87
77.73
82.85
83.94
13B
ED
84.69
89.05
89.61
Regular
82,47
76.76
79.34
79,98
7B
ED
86.12
86.41
86.09
Popular
Regular
84,30
77.73
80.77
81.51
13B
ED
89.19
84.69
86.77
87.10
Regular
76.11
76,80
76.26
76.03
ED
79.75
86.47
81.90
82.75
Adversurial
Regular
78.29
77.94
77.94
77.93
13B
ED
81.95
85.22
83.34
82.92
Table shows the ablation study results comparing Regular and ED across different model sizes.
We evaluate both methods using LLLVA-1.5 (Liu et al. 2024b) with 7B and 13B parameters on the
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POPE benchmark under random, popular, and adversarial settings. The metrics measured include precision, recall, score, and accuracy for each setting. The results demonstrate that ED consis- tently outperforms Regular across all settings and metrics, regardless of model size. This confirms that ED effective irrespective model capacity, and its performance improves as the model size increases generally.
D MORE RESULTS ON POPE
Table 9: Detailed results of POPE using LLaVA-1.5 7B. The numbers in the table represent the mean, and the values in parentheses indicate the standard deviation.
Dataset
Setting
Decoding Strategy
Precision
Accuracy
Regular
92.13 (0.54)
72.80 (0.57)
81.33 (0:41)
83.29 (0.35)
Random
ED
96.65 (0.52)
82.00 (0.52)
88.72 (0.18)
89.58 (0.16)
Regular
88.93 (0.60)
72.80 (0.57)
80.06 (0:05)
81.88 (0.48)
MSCOCO
Populor
ED
92.67 (0.18)
82.00 (0.52)
87.01 (0.27)
87.76 (0.22)
Regular
83.06 (0.58)
72.75 (0.59)
77.57 (0:57)
78.96 (0.52)
Adversarial
ED
87.05 (0.76)
82.00 (0.53)
84.75 (0.29)
81,90 (0.32)
Regular
87.24 (0.68)
78.36 (0.54)
82.56 (0:50)
83.45 (0.48)
Random
ED
91.62 (0.42)
89.67
90.63 (0.24)
90.73
Regular
80.85 (0.31)
78.36 (0.54)
79.59 (0.37)
79.90 (0.33)
A-OKVQA
Popular
84.27 (0.31)
89.67 (0.47)
86.89 (0.10)
86.47 (0.09)
Regular
72.08 (0.53)
78.48 (0.38)
75.15 (0:23)
74.04 (0.34)
Adversarial
75.00 (0.18)
89.78 (0.47)
81,72 (0.31)
79.92 (0.30)
Regular
87.16 (0.39)
79.12 (0.35)
75.15 (0.23)
74.04 (0.34)
Random
ED
91.91 (0.47)
87.58 (0.20)
89.69 (0.33)
89.93 (0.34)
Regular
77.64 (0 (0.26)
79.12 (0.35)
78.37 (0.18)
78.17 (0.17)
GQA
Popudor
ED
81.41 (0.75)
87.58 (0.20)
84.38 (0:43)
83,79 (0.52)
Regular
73.19 (0.49)
79.16 (0.35)
76.06 24)
75.08 (0.33)
Adversarial
ED
77.20 (0.30)
87.62
82.07 (0.42) 80.87 (0.37)
As shown in Table we report the precision, recall, F1 score, and accuracy for each different setting of POPE. To verify the consistency of ED in object hallucination tasks, we repeat the implementa- tions three times each and report the mean and standard deviation. The results demonstrate that ED consistently performs better than Regular in object hallucination tasks.
E PROMPT AND ADDITIONAL CASES OF GPT-AIDED EVALUATION
The prompt used for the evaluation of LLaVA-Benet captioning is presented in Table 10 It pro- vides the instructions necessary for assessing the quality of model-generated responses. This prompt is given to GPT-4 without disclosing which model generated the captions for the two assistants, and it is scored on a scale to 10. For qualitative analysis, examples comparing Regular with ED and Regular with FastED evident that ED and FastED produce better responses compared to Regular, as indicated by their ability to generate more accurate and detailed answers. Specifically, GPT-4 assigns significantly higher scores to ED in terms of accuracy and detailedness, validating the effectiveness of the ED approach. The comparison across different configurations.
presented in Tables I1 and T2 respectively. From these examples, it
experimental settings are applied evaluate AGLA and it enables direct
lava http:/11aaabbbcc--aaa in-the-withee
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Table 10: Prompts for GPT-aided evaluation on LLa VA-Bench.
Instructions:
You are an Al designed to evaluate and score the performance of two AI assistants describing given image. Your primary focus on the accuracy and detailedness of their descriptions. You will assess the accuracy by checking for hallucinations- any part of the description that inconsistent with the image content. For detailedness, you will consider how rich the response is in necessary details, excluding any hallucinated parts. You will provide scores on scale from I After scoring, you will offer explanation for your evaluation, ensuring it is free from bias and not influenced by order of presentation of the responses.
for each assistant separately, based on these criteria.
Input format:
[Assistant 1]
[Response 1}
[End of Assistant 1]
[Assistant 2]
[Response 2}
[And of Assistant 2]
Output format:
Accuracy:
Scores of the two
Reason:
Detailedness:
Scores of the two answers:
Reason:
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Table 11: Case of GPT-aided evaluation, where Assistant 1 is Regular and Assistant 2 is ED.
Input:
[Assistant 1]
In this image, there a creepy sculpture or figure of a pig with bright flames or fireworks coming out of its ears and appearing to extend from its tail. The personification of the pig is quite intriguing and unique, giving an impressive appearance. The floor and the pig sculpture both have reflective and shiny appearance, contributing to the visual appeal of the scene. This strange creation can likely be found a dimly-lit room or an art installation, capturing the attention and curiosity of those who encounter it.
[End of Assistant 1]
[Assistant 2]
The image features a red and orange fire- treebbeethhng lizard statue, possibly made of plastic, standing on a white surface. The lizard statue appears to on fire, with flames coming from its mouth. has pair of glasses its face, adding a unique touch to its appearance. The statue's legs are positioned a way that it appears to be walking on the ground, giving a dynamic and eye-catching presence.
[End of Assistant 2]
GPT-4 Output:
Accuracy:
Assistant 1: 2, Assistant 2: 8
Reason: Assistant I's description contains significant inaccuracies. The sculpture is de- scribed as a pig and mentions flames or fireworks coming out of its ears and tail, which is not the case in the image. This represents a considerable amount hallucination. On the other hand, Assistant correctly identifies the main features of the image, describing it as fire-breathing lizard statue with flames coming from various parts of its body, while also noting the glasses. These details match better with the actual image.
Detailedness:
Assistant 1: 4, Assistant 2: 6
Reason: While Assistant 1 provides a rich narrative, it is filled with inaccuracies that detract from its usefulness. The mention of a creepy sculpture, a dimly-lit room or an art installation, and reflective and shiny appearance of the floor add unnecessary and incorrect details that do not match the image. Assistant 2's description, though some- what simpler, is more accurate and focuses pertinent aspects such the lizard's color, material, and the presence of glasses. However, could have included more detailed ob- servations about the surface and environment found in the image. Therefore, Assistant 2 receives a better score for its detailedness.
20

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Published as a conference paper at ICLR 2025
Table 12: Case of GPT-aided evaluation, where Assistant is Regular and Assistant is FastED.
Input:
[Assistant 1]
The image features a table with delicious meal being prepared, showcasing a variety of cooked and sliced seafood. Steamed crayfish on the table, accompanied by lemon wedges, add lemon zest and freshness to the meal. There are five large crayfish in the image, with three of them laying in close proximity next each other, and the remaining two placed further apart. Alternatively, they can be seen alongside five fresh lemon wedges, with some placed directly on top the crayfish. The combination of the seafood and the lemon enhances the overall appeal and taste of this seafood meal.
[End Assistant 1]
(Assistant 2.
The image features a close-up of several pieces of cooked lobster, likely grilled or roasted, with a generous amount of seasoning. The lobster picces are placed on a tray, and there are couple of lemon wedges scattered around the tray, adding touch of freshness and acidity to the dish. The lobster pieces are arranged in various positions, with some overlapping and others sitting next each other. The overall presentation of the lobster and lemon wedges creates an appetizing and inviting scene.
[End of Assistant 21
GPT-4 Output:
Accuracy:
Assistant 1: 6, Assistant 2:
Reason: Assistant incorrectly identifies the seafood crayfish instead of lobster. Addi- tionally, there is a mention of five crayfish, but the image clearly depicts only four lobster tails. Assistant correctly identifies the seafood as lobster and describes the seasoning. which is consistent with the image, though the specifics of the cooking method (grilled or roasted) are not directly clear.
Detailedness:
Assistant 1: 7, Assistant 2: 8
Reason: Assistant provides information about the arrangement of the seafood and the presence of lemon wedges, albeit with some inaccuracies regarding the number of items and the type of seafood. Assistant gives more accurate and detailed description of the lobster's s appearance and seasoning, though lacks some detail about the exact arrangement or the garnishes present, compared to Assistant 1. Both assistants mention the lemon wedges and the general presentation, but Assistant 2's description aligns more closely with the actual content of the image.
21


Reviews:
[{"review_id": "9qtBu1UvD3", "reviewer": "Reviewer_2dSM", "summary": "This paper proposes a new Ensemble Decoding (ED) strategy to mitigate the object hallucination issue with Large Vision-Language Models (LVLMs). The proposed approach is motivated by the hypothesize that irrelevant objects and low object resolution in images are likely to impact performance negatively. In ED, the input image is split into sub-images and combines logit distributions by assigning weights through the attention map. ED adaptive plausibility constraint is proposed to calibrate logit distribution. The FastED, an optimized variant of ED, balances performance with speed by selecting a sub-image with the highest mean attention score from the original image. ED shows improved performance in several benchmarks", "questions": "1. it is unclear how to get the $d\\times d$ attention matrix in L161, Section 3.1, where d is the number of image patches. Can the author elaborate the dimension of $Q_t$ and $K_t$ in detail?\n2. As for the logit ensemble, the generated logit should has the same of the size of LLM tokenzier? It is unclear why the proposed adaptive plausibility constraint improves performance by only keeping the elements in #p_ED# which are larger than a threshold?\n3. Do we have any assumption that the length of the generated tokens have the same length? How should we do the logit ensemble when we have different number of generated tokens for different images.\n4. Even though the proposed method is training-free, it also introduces a lot of hyper-parameters $\\alpha$, $\\beta$, $N$, $H$ and $K$. how to tune these hyper-parameters is unclear.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The ensemble decoding strategy proposed in this work is well-motivated and sound by zooming into local regions which is more important for decoding at each decoding step. The proposed method also does not require any training.\n2. Starting from the baseline model, several optimization techniques have been proposed to improve model accuracy and running efficiency, including adaptive plausibility constraint to only keep the most plausible tokens, and a fast version ED to only focus on one particular sub-region in the image.\n3. Strong performance and new state-of-the-arts have been reported on several benchmarks.", "weaknesses": "The proposed ensemble encoding strategy, fuses the logits extracted from the original image and multiple local crops. This method makes sense, but introduces much more computation burden for real-world deployment. The FastED only processes one local crop and have some accuracy drops. However, one simpler alternative is to move the ensemble to the input, and concat the multiple images in the prompt. We can imagine that this method is much faster than ED/FastED because only one feed-forward is needed. There is no discussion about where to do the ensemble.", "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": "aKr3UMpIrm", "reviewer": "Reviewer_mCVa", "summary": "This paper addresses the issue of object hallucination in Large Vision-Language Models (LVLMs), where models generate descriptions that include nonexistent objects or misrepresent existing ones. While previous approaches such as data augmentation and training-free methods have attempted to mitigate this problem, they face scalability issues and often rely on external modules. The authors propose a new method called Ensemble Decoding (ED), which divides input images into sub-images and combines logit distributions by weighting them through an attention map. Additionally, the paper introduces ED’s adaptive plausibility constraint to calibrate logit distributions and a variant named FastED for speed-critical applications. Extensive experiments demonstrate that this method achieves state-of-the-art performance on hallucination benchmarks, confirming its effectiveness in reducing object hallucinations.", "questions": "- Was there any weighting operation applied during the ensemble process?\n- Can the authors discuss the relationship between this method and model uncertainty? When we perform multiple forward passes on the model and take the average, we are essentially obtaining model uncertainty. Can this method be understood as adding more logical constraints, but essentially optimizing the answer through model uncertainty?\n- Besides logit ensemble, are there other levels of ensemble that could be attempted?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The idea of Ensemble Decoding (ED) is interesting, and well motived by evidences of model will get right answer after applying crop and resize to the image and the toy experiment of checking whether properly divided sub-images can reduce object hallucination in the outputs o LVLMs\n- The authors conducted extensive ablation studies and main experiments to validate the effectiveness of the method, and the experimental results appear to be quite convincing.", "weaknesses": "- There doesn’t seem to be a deeper explanation for why this method works. It would be better if the authors could provide more insights.\nHow do the degree of cropping and resizing, as well as the attention weights, affect the results? I don’t seem to see any related discussion or analysis on this.\n\n- How do the degree of cropping and resizing, as well as the attention weights, affect the results? I don’t seem to see any related discussion or analysis on this.", "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": "9IhPABkpqS", "reviewer": "Reviewer_98vf", "summary": "Large Vision-Language Models (LVLMs) excel at tasks like image captioning but face challenges with object hallucination, where they describe objects that aren't actually present in images. While existing solutions like data augmentation have attempted to address this issue, they face scalability problems and often require additional modules. This paper introduces Ensemble Decoding (ED), which works by dividing input images into smaller parts and combining their logit distributions using attention map-based weighting. It also develops ED adaptive plausibility constraint for logit distribution calibration and a faster variant called FastED for time-sensitive applications. Through extensive testing on hallucination benchmarks, the proposed method demonstrates superior performance compared to existing approaches.", "questions": "When splitting the original image into sub-images, is there no overlap at all between each sub-image?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. It is interesting to explore the effect of the number of unnecessary objects in an image and the object resolution on the performance of LVLM.\n2. The proposed method is simple and straightforward to implement, which enhances its reproducibility.\n3. Experiments on multiple benchmarks demonstrate that the proposed method can achieve better performance than state-of-the-art work.", "weaknesses": "I have some concerns about this paper:\n\n1. From Figure 3, it seems that feeding multiple sub-images into LVLM gives more correct answers than feeding the original image. I'm curious about the performance of ED without using the original image.\n2. If LVLM produces the correct output for the original image, but produces the wrong output for the sub-image, does ED negatively affect the understanding of the model in this case? For example, can the split sub-image get a valid output when the target object is located at the centre of the image?\n3. It is interesting that low-resolution objects may trigger the hallucination of large models. However, dividing the image into small sub-images does not improve the absolute resolution of the object but rather the percentage of the object in the image.\n4. How is the metric of inference latency calculated? ED only slightly outperforms AGLA in terms of recall at the cost of more than twice the inference latency; FastED is close to AGLA in terms of efficiency but has lower recall. So what is the advantage of the method proposed in this paper over AGLA? The authors only show the performance of FastED on the CHAIR dataset, which makes its performance difficult to evaluate.\n5. In this paper, there is a lack of analysis and ablation experiments on the impact of the constituent modules in ED (e.g. Attention-Guided Weight, and Adaptive Plausibility Constraint) on the performance of the method.\n6.The exact number of sub-images into which the image is split (determined by the hyperparameter N) may have a strong impact on the efficiency and performance of the ED, and the impact of this factor is not analysed in this paper.\n7. From Table 4, the improvement from aggregating the logits of sub-images does not seem to be significant.", "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": "6eOdIiRq1F", "reviewer": "Reviewer_UHSv", "summary": "This paper introduces a technique (ED) to reduce hallucination in multi-modal large language models (MLLMs) by splitting an image into N sub-images followed by using an attention guided ensemble decoding approach which ensembles the logits for the next token to be predicted by the MLLM using the attention weights of the original full-sized image and the N sub-images with the text prior. The authors conduct a systematic study which shows the two main causes of object hallucination in MLLMs as the number of objects in an image and the image resolution. They also provide systematic quantitative metrics which show improvement on hallucination benchmarks compared to existing SoTA methods and regular decoding approaches. The authors present a latency analysis of their method compared to other decoding based methods and provide a alternative baseline of FastED which balances accuracy with speed. Overall the paper is well written and conducts systematic experimental analysis.", "questions": "Please refer to the weaknesses section for questions. Additionally: \n\n1) Have you tried the recent Qwen-2 VL model with your technique? Given that it does not use a Q-former based technique, it would be interesting to see how this method works with a model architecture other than LLava\n2) If possible, could you provide some additional examples of attention maps and final attention weights for the N sub-images for different sets of prompts and input images? It would be helpful to understand how exactly the attention weights vary.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1) The paper is very well written and it is easy to understand their method. \n2) The approach presented is easy to implement, is training-free and only requires inference time re-weighting of logits. \n2) The pilot study which talks about the main causes of object hallucination in MLLMs is instructive and useful to the community as a whole. \n3) It is easy to understand the motivation of their method from the conclusions of the study they present, which shows that masking irrelevant regions of an image helps reduce object hallucination, and their approach of ensembling logits from various sub-images should help with that\n4) The results achieve SoTA performance on commonly used benchmarks to measure hallucination (POPE and CHAIR)", "weaknesses": "1) While the idea of splitting the original image into N sub-images is well motivated from the pilot study, the approach to utilize the attention weights to re-weight the logits lacks theoretical/empirical justification. Even intuitively, it would make sense to use attention weights for prompts such as \"is there a spoon in the image\" to put a higher emphasis on the sub-image which contains a spoon. However, for generic prompts such as \"describe the image\", the attention weights across the sub-images should be more or less equal. In such cases I am not sure of the reason why it might be useful to weight the logits by the attention scores. \n2) Adding on to 1, the paper lacks experiments/ablations which justify the use of attention weights for the decoding process. Adding ablations which vary the temperature parameter might be useful to understand the impact of attention on results. For example, a useful result to conclude the validity of the attention weights could be setting very high values of temperature parameter leading to sampling from uniform distribution across the sub-images. \n3) The paper does not include experiments on tasks other than hallucination and image captioning. For example on the MME benchmark, out of the 16 tasks, the authors only include results on the existence, count, position, and color sub-categories. It would be good to look at the results on some of the other sub-tasks such as code reasoning, OCR, common-sense reasoning, text translation etc. (as done in the VCD paper) to ensure that this method of decoding does not regress the performance on such benchmarks. \n4) The authors show results for FastED only on CHAIR and LLava-Bench, it would be good to see results of FastED on POPE and MME benchmarks as well \n5) This is already mentioned in the limitations section, but the method only works for MLLM architectures which use a linear projector and does not work with resampler and Q-former based architectures (BLIP-2, Qwen-VL). This limits the generalizability of the technique across a wide-range of model architectures.", "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)