Title: What Do Deep Saliency Models Learn about Visual Attention?
Venue: NeurIPS 2023
Accepted: yes
Keywords: Saliency prediction

human attention

low-level vision
Abstract: In recent years, deep saliency models have made significant progress in predicting human visual attention. However, the mechanisms behind their success remain largely unexplained due to the opaque nature of deep neural networks. In this paper, we present a novel analytic framework that sheds light on the implicit features learned by saliency models and provides principled interpretation and quantification of their contributions to saliency prediction. Our approach decomposes these implicit features into interpretable bases that are explicitly aligned with semantic attributes and reformulates saliency prediction as a weighted combination of probability maps connecting the bases and saliency. By applying our framework, we conduct extensive analyses from various perspectives, including the positive and negative weights of semantics, the impact of training data and architectural designs, the progressive influences of fine-tuning, and common error patterns of state-of-the-art deep saliency models. Additionally, we demonstrate the effectiveness of our framework by exploring visual attention characteristics in various application scenarios, such as the atypical attention of people with autism spectrum disorder, attention to emotion-eliciting stimuli, and attention evolution over time. Our code is publicly available at \url{https://github.com/szzexpoi/saliency_analysis}.

What Do Deep Saliency Models Learn about Visual Attention?

Abstract

In recent years, deep saliency models have made significant progress in predicting human visual attention. However, the mechanisms behind their success remain largely unexplained due to the opaque nature of deep neural networks. In this paper, we present a novel analytic framework that sheds light on the implicit features learned by saliency models and provides principled interpretation and quantification of their contributions to saliency prediction. Our approach decomposes these implicit features into interpretable bases that are explicitly aligned with semantic attributes and reformulates saliency prediction as a weighted combination of probability maps connecting the bases and saliency. By applying our framework, we conduct extensive analyses from various perspectives, including the positive and negative weights of semantics, the impact of training data and architectural designs, the progressive influences of fine-tuning, and common failure patterns of state-ofthe-art deep saliency models. Additionally, we demonstrate the effectiveness of our framework by exploring visual attention characteristics in various application scenarios, such as the atypical attention of people with autism spectrum disorder, attention to emotion-eliciting stimuli, and attention evolution over time. Our code is publicly available at https://github.com/szzexpoi/saliency_analysis .

1 Introduction

Attention deployment is a complex and fundamental process that enables humans to selectively attend to important sensory data in the visual environment. Scientists have been fascinated by the question of what drives human visual attention for decades. Understanding the mechanisms of visual attention not only sheds light on the human visual system but also helps computational methods to localize critical sensory inputs more efficiently.

One approach to predicting human attention is through saliency models, which have received considerable research attention. A saliency model predicts the most visually important regions in an image that are likely to capture attention. Earlier models follow a feature integration approach [1, 2, 3, 4] and extract low-level features ( e.g., colors, intensity, orientations) or higher-level features ( e.g., objects, semantics) from the input image to infer saliency [5, 6, 7]. While these models show initial success, their performance is limited by the difficulty of engineering relevant visual features. In contrast, recent saliency models [8, 9, 10, 11] follow a data-driven approach, leveraging large datasets [12] and deep neural networks [13, 14, 15] to learn discriminative features. These models achieve human-level performance on several saliency benchmarks [12, 16, 17], thanks to their accurate detection of important objects and high-level semantics [18, 19]. However, due to the lack of transparency, it is still unclear what semantic features these models have captured to predict visual saliency.

To understand how deep neural networks predict visual saliency, in this paper, we develop a principled analytic framework and address several key questions through comprehensive analyses:

How do deep saliency models differentiate salient and non-salient semantics?

How do data and model designs affect semantic weights in saliency prediction?

How does fine-tuning saliency models affect semantic weights?

Can deep saliency models capture characteristics of human attention?

What is missing to close the gap between saliency models and human attention?

Our method connects implicit features learned by deep saliency models to interpretable semantic attributes and quantifies their impact with a probabilistic method. It factorizes the features into trainable bases, and reformulates the saliency inference as a weighted combination of probability maps, with each map indicating the presence of a basis. By measuring the alignment between the bases and fine-grained semantic attributes ( e.g., concepts in Visual Genome dataset [20]), it quantifies the relationships between diverse semantics and saliency. This unique capability enables us to identify the impact of various key factors on saliency prediction, including training datasets, model designs, and fine-tuning. It can also identify common failure patterns with state-of-the-art saliency models, such as SALICON [9], DINet [11], and TranSalNet [10]. Beyond general saliency prediction, the framework also shows promise in analyzing fine-grained attention preferences in specific application contexts, such as attention in people with autism spectrum disorder, attention to emotional-eliciting stimuli, and attention evolution over time. In sum, our method offers an interpretable interface that enables researchers to better understand the relationships between visual semantics and saliency prediction, as well as a tool for analyzing the performance of deep saliency models in various applications.

2 Related Works

Our work is most related to previous studies on visual saliency prediction, which make progress in both data collection and computational modeling.

Saliency datasets. With the overarching goal of understanding human visual perception, considerable efforts have been placed on constructing saliency datasets with diverse stimuli. The pioneering study [16] proposes an eye-tracking dataset with naturalistic images, which later becomes a popular online benchmark [21]. Several subsequent datasets characterize visual saliency into finer categories based on visual scenes [22], visual semantics [17], sentiments [23, 24], or temporal dynamics [25], to study the impact of different experimental factors on attention. To overcome the difficulties of tracking eye movements, Jiang et al. [12] leverage crowd-sourcing techniques and use mouse-tracking as an approximation for eye gaze, which results in currently the largest saliency dataset. Recent works also consider broader ranges of visual stimuli, including videos [26, 27], graphical designs [28, 29], web pages [30, 31], crowds [32], driving scenes [33] and immersive environments [34, 35]. In this study, we focus on visual saliency for naturalistic images, which serves as the foundation of saliency prediction studies.

Saliency models. Prior works have developed saliency prediction models to quantitatively study human attention. Inspired by seminal works on saliency modeling [5, 6, 36], early saliency models [1, 2, 3, 4, 37] typically adopt a bottom-up approach, integrating handcrafted features ( e.g., colors, intensity, and orientations). On the other hand, recent approaches take a different route and leverage deep neural networks [13, 14, 15, 38] to automatically learn features and predict saliency. Vig et al. [39] is one of the first attempts to utilize convolutional neural networks (CNNs) for saliency prediction. Huang et al. [9] consider features learned from multi-scale inputs to model the coarseto-fine semantics. Kruthiventi et al. [40] leverage convolutional layers with diverse kernel sizes to capture multi-scale features and incorporate positional biases with location-dependent convolution. Kümmerer et al. [41] demonstrate the usefulness of features of visual objects in saliency prediction. Cornia et al. [8] develop a recurrent neural network to iteratively refine features for saliency prediction. Yang et al. [11] improve saliency prediction with dilated convolution to capture information from broader regions. Lou et al. [10] study the usefulness of self-attention [15] for saliency prediction. Instead of building new models, several works [18, 42, 43, 44, 45, 46] study the behaviors of models. By analyzing predictions on different categories of stimuli, they identify key factors behind the successes and failures of existing saliency models ( e.g., accurate detection of semantic objects, incorporation of low- and high-level features, contrast between diverse visual cues, detection of Odd-One-Out target, and etc.), and propose directions for improvements. A recent work [19] also

IoU

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Semantic Alignment

Basis 1: 80% face + 10% female + 5% happy + ...

Basis 2: 30% sky + 5% cloudy + 2% blue + ...

Basis 3: 70% jersey + 10% black + 5% white + ...

Basis N: 60% hair + 25% black + 10% brown + ...

...

face

happy

female

sky

blue

cloudy

hair

black

black

white

Tola

ulol

analyzes the features learned by saliency models by aligning activation maps with segmentation for a selection of objects ( e.g., body parts, food, and vehicles in [17]).

Our research contributes to the field of attention research by introducing a rigorous methodology for analyzing deep saliency models. In contrast to previous studies [18, 19, 42, 43], our study has three key distinctions: First, while previous analyses of deep saliency models were restricted to a predefined set of salient objects ( e.g., object segmentation used in [19]), our method automatically identifies both salient and non-salient semantics from a vocabulary of objects, parts, actions, and attributes. Second, different from previous qualitative analyses, our method quantifies the weights of these semantics in saliency prediction. It allows us to investigate the impacts of various factors ( e.g., the contributions of positive/negative semantics, the characteristics of datasets and model designs, and the process of fine-tuning) on saliency prediction, offering insights into the development of future deep saliency models. Third, our approach goes beyond analyzing the general visual saliency and demonstrates its strength in characterizing human visual attention under specific conditions such as the attention of people with autism spectrum disorder, the saliency of emotion-eliciting images, and time-course attention evolution.

3 Methodology

Human visual attention is influenced by a spectrum of visual cues, from low-level contrasts to highlevel semantic attributes [5]. However, current deep learning-based saliency models [8, 9, 11] remain opaque in terms of the semantic attributes they have learned and how these attributes contribute to saliency prediction. To address this gap, we propose a method that decomposes neural network features into discriminative bases aligned with a wide range of salient or non-salient semantic attributes, and quantifies their weights in saliency prediction.

As illustrated in Figure 1, our method decomposes visual features by projecting them onto a collection of trainable bases, and uses the probabilistic distribution of bases to infer visual saliency. The overall idea is to identify both salient and non-salient semantics and quantify their impact on saliency prediction. To achieve this, we start with a deep saliency model and compare the features learned at the penultimate layer with different bases. This is done using a dot product between features V ∈ R M × C ( M and C are the spatial resolution and dimension of features) and bases B ∈ R N × C ( N = 1000 is the number of bases defined based on the number of units in the final layers of deep saliency models [9, 10]), which corresponds to their cosine similarity:

where ⊗ denotes the dot product. σ is the Sigmoid activation for normalization. α i,j ∈ [0 , 1] represents the probability of j th basis b j detected at the i th region P ( b j = 1 | V i ) . Inspired by [47] for decomposing model weights, we factorize the features as a weighted combination of matched bases:

where V f ∈ R M × C are factorized features used to predict the saliency map S = W f V f ( S ∈ R M , W f are weights of the last layer).

Upon building the connections between visual features and discriminative bases, we then re-route the final saliency prediction by (1) freezing all model weights including the bases, and (2) adjusting the last layer for saliency inference based on the probabilistic distribution α . We train a new layer (with weights W sal ) for predicting the saliency map:

Intuitively, the method formulates the problem of saliency prediction as learning the linear correlation between the detected bases and visual saliency, which can be denoted as learning P ( S | b 1 , b 2 , ..., b N ) . With the intrinsic interpretability of the design, i.e., α as the probabilistic distribution of bases and W sal encoding the positive/negative importance of bases, we are able to investigate the weights of different bases to visual saliency.

The final step is to understand the semantic meanings of each basis. Unlike previous studies [18, 19, 42, 43] that focus on predefined salient objects, we take into account a comprehensive range of semantics without assumptions on their saliency. Specifically, our method leverages the factorization paradigm to measure the alignment between each basis and the semantics. Given an image and the regions of interest for different semantics ( e.g., bounding box annotations in Visual Genome [20]), we (1) compute the probabilistic map for each j th basis α : ,j ∈ R M , and (2) measure its alignment O j,p with the regions of interest R p for each semantic p . Following [48], we binarize the probabilistic map with a threshold t j and measure the alignment with Intersection over Union (IoU):

We use an adaptive threshold t j for each individual basis, which is defined to cover the top 20% of regions of probabilistic maps. Through iterating the measuring process for all images within the dataset, we are able to link bases learned from saliency prediction to a variety of visual semantics. We consider the top-5 semantics matched with each basis, and incorporate their average alignment scores ˆ O j,p for determining the weight I to saliency:

where Z is the normalization factor that normalizes the contribution of the semantics to the range of [-1, 1] (for semantics with positive/negative contributions, Z denotes the maximal/minimum contribution among all semantics). This approach enables us to capture a broader range of contributing semantics, while avoiding an overemphasis on dominant salient/non-salient semantics (e.g., face and cloudness).

Overall, our method establishes the foundation for bridging implicit features learned by deep saliency models with interpretable semantics. It goes beyond existing studies that analyze models' predictive behaviors on a selection of object categories, as it considers a comprehensive range of semantics without assuming their relevance to saliency. It provides insights into how well the models capture both salient and non-salient semantics, and how they quantitatively contribute to saliency prediction.

having meeting

face

surfing

goggles

airplane

nose

beard

zebra

wetsuit

jumping

elephant

snowboarding

skiing

skateboarding

head

giraffe

double decker

train

written

blond

tinted

male

mouth

eye

helmet

teeth

barefoot

glowing

street_sign

letter

staring

floor

asian

hardwood

wall

wispy

balding

choppy

kitchen

posing

curly

road

field

bathroom

carpet

ceiling

happy

smiling

room

pavement

paved

park

asphalt

cloudy

sky

overcast

hazy

cloudless

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4 Experiment

4.1 Implementation

Semantic annotations. To correlate implicit features with interpretable visual semantics, we leverage the Visual Genome [20] dataset. It has (1) multiple objects in the same scene to derive relative importance; and (2) a broad coverage of semantics in the naturalistic context, including objects, parts, attributes, and actions. We use the bounding box annotations of semantics ( i.e., R in Equation 4) to measure the alignment between bases and semantics.

Model configuration. Weexperiment with three state-of-the-art saliency prediction models, including SALICON [9], DINet [11] and TranSalNet [10]. All models are optimized with a combination of saliency evaluation metrics ( i.e., Normalized Scanpath Saliency (NSS) [49], Correlation Coefficient (CC) [50], and KL-Divergence (KLD) [51]) as proposed in [8], and use ResNet-50 [13] as the backbone. Model training follows a two-step paradigm: (1) The model is optimized to factorize features with trainable bases, where the weighted combination of bases ( i.e., V f in Equation 2) is used for predicting the saliency map. Note that we do not use pretrained and fixed deep saliency models, but optimize the corresponding model architecture with the proposed factorization modules. (2) We freeze the model weights learned in the previous step and reroute the saliency inference to derive the saliency map from the probabilistic distribution α (see Equation 3) so that the interpretation is on features learned by the same saliency model. Only the last layer W sal is fine-tuned to learn the correlation between the distribution of bases and visual saliency.

4.2 How Do Deep Saliency Models Differentiate Salient and Non-Salient Semantics?

Deep saliency models are powerful tools for predicting visual saliency, and their ability to incorporate semantic information is crucial for closing the gap between computational modeling and human behaviors [17]. To gain insight into the semantics that deep saliency models learn and their contributions to saliency prediction, we apply our framework to the state-of-the-art DINet [11] trained on the SALICON [12] dataset (see Section 4.3 for results on other models and datasets), and explicitly measure the weights of diverse semantics during the inference process ( i.e., I p in Equation 5).

Figure 2 shows that the DINet model effectively captures a variety of semantics that are closely related to visual saliency. These include social cues such as faces, noses, and beards, actions like having a meeting, snowboarding, and jumping, clothing such as goggles, and salient object categories like animals, vehicles, and text. These findings resonate with previous research [18, 19, 43] that saliency models learn to recognize salient cues. More importantly, they showcase the versatility of our approach in automatically identifying key contributing factors of saliency models without any preconceived assumptions [43, 19], enabling attention analyses across a diverse array of scenarios (see Section 4.5).

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text

Another unique advantage of our approach is to simultaneously derive semantics that both positively and negatively contribute to saliency, while previous studies commonly focus on the positive side. Our results reveal a clear separation between the semantics that contribute positively and negatively to saliency. Specifically, the model considers social and action cues to have a positive contribution to saliency, while semantics related to backgrounds such as sky ( e.g., hazy and overcast), ground ( e.g., pavement and carpet), and scene ( e.g., bathroom and park) have negative weights. The observation demonstrates that deep saliency models' success is not only due to the accurate detection of salient objects [18, 19, 43], but also strongly related to the ability to distinguish salient and non-salient semantics.

4.3 How Do Data and Model Designs Affect Saliency Prediction?

Training data and model designs play crucial roles in determining how well deep saliency models perform [18]. To understand these roles better, we conduct a comparative analysis of the effects of different training datasets and model architectures on saliency prediction.

Figure 3 compares the results of three DINet models trained on different datasets: SALICON [12], OSIE [17], and MIT [16]. It reveals that the shifts in semantics weights are tightly coupled with the characteristics of the training data. For instance, the model trained on OSIE pays more attention to social cues and non-salient semantics, because the OSIE dataset collects attention on semanticrich stimuli with diverse social cues. Differently, the model trained on MIT assigns a less positive weight to social cues and a more negative weight to the vehicle category, which is likely due to the dataset's less emphasis on social semantics and the co-occurrence of vehicles and more salient objects. Therefore, differences in the semantic weights across models trained on different datasets reflect the variations in the semantics presented in the respective datasets.

Figure 4 compares the semantic weights of three different saliency models trained on the same SALICON [12] dataset: i.e., DINet [11], SALICON [9], and TranSalNet [10]. While all models place the highest weights on actions and the lowest weights on ground, scene, and sky, the differences in model designs are reflected in their semantic weights. For instance, compared to the other models, TranSalNet considers clothing and text to be strongly salient but sky to be less non-salient. The results

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shed light on the relationships between the designs and behaviors of saliency models, and show the usefulness of our framework in helping researchers tailor their models for specific applications.

Despite the differences in model behaviors across datasets and models, all models excel at automatically identifying salient semantics, e.g., action and social cues, and differentiating foreground from background semantics. The observation is consistent with our findings in Section 4.2, and indicates that the ability to correlate semantics with saliency and quantify their positive/negative contributions is a systematic advantage of deep saliency models.

4.4 How Does Fine-tuning Saliency Models Affect Semantic Weights?

The process of fine-tuning is crucial for training deep saliency models, as highlighted in previous studies [18, 19]. To better understand the evolution of feature weights during fine-tuning, we conduct experiments on models trained in three scenarios of fine-tuning. These scenarios include models with fixed ImageNet [52] features (W/o fine-tuning), models that are fine-tuned for a single epoch (Single-epoch fine-tuning), and fine-tuned models with the best validation performance (Complete fine-tuning). We experiment with three different models, namely SALICON [9], DINet [11], and TranSalNet [10], and report the average results obtained from our experiments.

As shown on the left side of Figure 5, models with fixed ImageNet features already have the ability to identify salient semantics, such as action and social cues, highlighted with strong positive weights. This is consistent with previous findings [19], which validates the effectiveness of our approach.

When comparing the results of models with single-epoch fine-tuning (shown in the middle of Figure 5) to those without fine-tuning (shown on the left), we notice a significant shift in the negative weights. Specifically, while models with fixed ImageNet features identify scene-related semantics to contribute most negatively to saliency, after being fine-tuned for a single epoch, the models now focus on sky-related semantics and have a weaker emphasis on scene-related semantics. This can be attributed to the fact that ImageNet features are learned from iconic images, which are inherently insensitive to the sky background. However, since the sky is a strong indicator of low saliency values, it is necessary to learn this semantic to achieve accurate saliency prediction (note that the largest performance gain also occurs during the first epoch).

Finally, when examining the fully-tuned models (shown on the right side of Figure 5), we find that semantic weights continue to evolve during the later epochs of fine-tuning. Unlike the first epoch which imposes larger changes on a few negative semantics, the subsequent fine-tuning mostly plays a role in refining the weights of a broader range of semantics, enabling the models to become more sensitive to salient cues ( e.g. action and text) and continuously adjusts the weights of negative semantics ( e.g. ground and other).

These observations offer a comprehensive view of how deep saliency models progressively adapt ImageNet features through fine-tuning. They show that fine-tuning first concentrates on semantics with negative weights to saliency, which are not well captured in the pretrained features, and then gradually adjusts the relative weights of diverse semantics. Understanding the evolution of model behaviors during training can provide insights into optimizing the learning recipes for saliency models and enabling them to progressively encode the knowledge of diverse semantics.

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4.5 Can Deep Saliency Models Capture Characteristics of Human Attention?

Human attention is influenced by several factors, such as the visual preferences of viewers, characteristics of visual stimuli, and temporal dynamics. We investigate the ability of DINet to capture the impact of these factors by training it on different attention data for each factor. We visualize results for the most discriminative semantics, and include the complete analysis in supplementary materials.

Firstly, we study the impact of visual preferences on attention with two subject groups, i.e., , people with autism and those without, using a dataset with attention data of subjects from the two groups [53]. We train a DINet model on each set to compare their corresponding semantic weights. As shown in Figure 6a, both models assign significant weights to social cues, but the model for the autism group has a considerably weaker emphasis on action cues. This can be linked to the deficits in joint attention for people with autism [54, 55]. Additionally, the model for the autism group also assigns a strong positive weight to the 'other' category. The specific objects highlighted in the category are related to gadgets, e.g., digital, illuminated, and metal (see supplementary materials for details), which is consistent with the findings about the special interests of people with autism [53].

Next, we explore the impact of stimuli with diverse characteristics with images exhibiting positive, negative, and neutral sentiments, using the EMOd dataset [23]. Figure 6b shows that models trained on stimuli eliciting strong emotions (positive and negative) exhibit a higher emphasis on social and action cues ( e.g., face and nose, see our supplementary materials for details) than the one for neutral sentiment. Their behaviors align with previous findings that human attention generally prioritizes emotion-eliciting content over non-emotion-eliciting content ( e.g., clothing) [56, 57]. We also identify that models for positive/negative sentiments place more focus on human-related objects ( e.g., social cues and clothing) than objects less related to humans ( e.g., animals), which is a driving factor for characterizing the emotion prioritization effect on attention deployment.

Finally, we investigate the effects of temporal dynamics by conducting experiments on the CodeCharts1K [25] dataset that provides attention annotations collected at 0.5, 3, and 5 seconds. As depicted in Figure 6c, a shift of focus from dominantly salient cues ( e.g., action and social cues) to more diverse semantics ( e.g., vehicle, animal, and color) is depicted by the gradually increasing weights for the latter group. It is because viewers usually engage their attention to the most salient cues at the beginning of visual exposure ( i.e., within 3 seconds) before broadening their focus.

Overall, our findings suggest that deep saliency models can encode the fine-grained characteristics of diverse attention. They also validate the usefulness of our approach in revealing the discriminative patterns of attention and shed light on how visual attention is influenced by various factors.

4.6 What Is Missing to Close the Gap Between Saliency Models and Human Attention?

Previous studies [18, 43] have identified a collection of common mistakes for saliency models by probing the predicted saliency maps. In this paper, we aim to complement these studies by analyzing the failure patterns within the intermediate inference process using the proposed factorization framework.

Prediction

Distribution

of Bases

Successful Examples

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1

To conduct our analysis, we first select the common success and failure examples where three tested models ( i.e., SALICON [9], DINet [11], and TranSalNet [10]) consistently have high/low NSS scores [49]. Then we perform a qualitative analysis by visualizing the spatial probabilistic distribution ( α in Equation 3) of the bases for semantics with positive (red) and negative (blue) weights (see our supplementary materials for details), which is used to derive the final saliency maps.

In the successful examples (see the left panel of Figure 7), we find that accurate saliency prediction correlates with the differentiation of diverse semantics. Specifically, stimuli for these examples typically have salient and non-salient regions belonging to different semantics. Therefore, with the ability to distinguish positive and negative semantics ( i.e., with discriminative distributions of the corresponding bases), models can readily determine their saliency distribution.

However, in the failure examples (see the right panel of Figure 7), models commonly struggle to determine the saliency within objects ( e.g., bicycle in the 1 st failure example) or among objects with similar semantics ( e.g., elephants in the 2 nd failure example). Investigation of the probabilistic distribution of bases shows that models typically have a uniform-like distribution of bases on object parts or among objects of the same category, thus inherently incapable of determining the importance of the bases to construct an accurate saliency map. We also note that existing models have difficulty with scenes without salient objects, which is illustrated in the 3 rd failure examples with a relatively empty scene.

As shown in Figure 7) (second column of the right panel), the ground truth human attention of the failure patterns is scattered. We further look into the inter-subject variability of these cases and found that their inter-subject variability is high, suggesting that human viewers may not agree on where to look, and therefore the ground truth maps are less valid. In this case, one assumption about saliency modeling (i.e., certain commonality about human attention patterns) may not be true, and the validity of using the ground truth human map for training and evaluation (i.e., the standard leave-one-subject-out approach), and the expected behavior of targeted models are interesting and open questions.

Overall, while high-level semantics learned in existing deep saliency models are powerful, we hypothesize that leveraging more structured representations to encode the contextual relationships between semantics, and integrating mid- and low-level cues will be helpful for accommodating challenging scenarios in saliency prediction.

4.7 Quantitative Evaluation of the Interpretable Model

The fundamental objective of our study is to develop a principled framework for understanding the underlying mechanism behind deep saliency models without altering their inherent behaviors. For this, we only introduce minimal architectural modifications, limited to the last two layers of the saliency models, thereby ensuring that their performance aligns seamlessly with the original models across all datasets. To complement our aforementioned analyses and further substantiate the efficacy

of our methodology, we quantitatively evaluate the saliency prediction performance of our method (using a DINet backbone trained the SALICON training split as an example) on three commonly used saliency datasets, including OSIE [17], MIT [16], and SALICON [12]. Comparative results reported in Table 1 demonstrate the competitive nature of our approach with respect to state-of-the-art methods, and validate its effectiveness in achieving the balance between interpretability and model performance.

5 Conclusion, Limitations, and Broader Impacts

As deep saliency models excel in performance, it is important to understand the factors contributing to their successes. Our study introduces a novel analytic framework to explore how deep saliency models prioritize visual cues to predict saliency, which is crucial for interpreting model behavior and gaining insights into visual elements most influential in performance. We discover that the models' success can be attributed to their accurate feature detection and their ability to differentiate semantics with positive and negative weights. These semantic weights are influenced by various factors, such as the training data and model designs. Furthermore, fine-tuning the model is advantageous, particularly in allocating suitable weights to non-salient semantics for optimal performance. Our framework also serves as a valuable tool for characterizing human visual attention in diverse scenarios. Additionally, our study identifies common failure patterns in saliency models by examining inference processes, discusses challenges from both human and model attention, and suggests modeling with holistic incorporation of structures and lower-level information.

Despite advancing attention research from different perspectives, our work still has room for improvement. Specifically, the current study focuses on visual attention deployment in real-world scenarios and employs the commonly used natural scenes as visual stimuli. We are aware that certain applications ( e.g., graphical design and software development) may also involve artificial stimuli, such as advertisements, diagrams, and webpages, and extension of the proposed framework to broader domains can be straightforward and interesting.

We anticipate several positive impacts stemming from this research. Firstly, by advancing the understanding of deep saliency models, valuable insights can be applied to optimize interfaces for human-computer interactions. This, in turn, will enhance the efficiency and reliability of the next generation of computer-aided systems, leading to improved user experiences and increased productivity. Secondly, the accurate capture of visual importance can have significant implications for individuals with visual impairments. Saliency models can effectively assist visually impaired individuals in navigating and interacting with both people and environments. This could empower them to engage more fully in various aspects of daily life, fostering independence and inclusivity. In sum, this comprehensive exploration of computational saliency modeling holds great potential for broader societal benefits.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new framework, which can decompose the learned features of a saliency model into trainable bases (using [42]), those bases are combined to formulate the final saliency map, and the weight of the combination indicates the contribution of each basis. The semantic meaning of each basis can be explored by matching the bases to a probe dataset (Visual Genome is used in this draft), Thus, saliency can be studied based on visual concepts, .e.g, different types of visual relationship, or objects, exploring what the key factors are behind the saliency region.\n\nSTRENGTHS:\nThis draft is well written, the organization is clear, and the method is explained in details. The proposed method attempts to solve a fundamental and important question, what features are important to saliency.\n\nWEAKNESSES:\nI like this study attempts to solve a very interesting question, however, some of the designs may need more discussion. This study is based on the existing basis de-composition[42] technique. The main contribution is to link those bases to other visual concepts by computing thresholded IOU. This design heavily relies on the probing dataset, which makes the hyper-parameter threshold a little tricky. What if other datasets are used, will the bases also match other concepts, how one can validate the matched concept (objects or events) is valid. Some studies attempt to explore the learned feature based on known probing concepts, e.g., designing a dataset specifically for texture and shape concepts(Geirhos, Robert, et al. 2018). Exploring visual concepts (bases) by matching to other datasets is not very convincing. Should one exhaustively match to all concepts to find the best match (I highly doubt if this is feasible or how to define the universe), or setting a threshold and matching to a “local”’ solution(a specific dataset)? Thus, matching bases to a specific dataset is not very solid to show what the bases are. \n\nMore generally, assuming we can find the “ground-truth” (the true meaning of those bases), I believe saliency is way more complicated than simply based on visual concepts. From Figure 2, we can see that street sign is less important than faces. We can imagine the street sign could be the most salient region if the picture only contains a street sign in the middle. Given two images, one has a face in the middle and one has a street sign in the middle, they could be equally salient. Thus, one explanation for figure 2 could also be it shows the bias of the dataset which used to train the model, more faces appeared in that dataset. There also could be biases in the probe dataset, Visual Genome in this study. One concept, say “having meeting”, is more salient could be the visual features happen to match one of the bases. The real most salient basis does not exist in that dataset. \n\nAnother problem in this study is that saliency may not depend on the semantic. Imagine we have ten faces in a picture, the middle could be the most salient, while the others are not. Thus, study which visual basis is the most salient is not convincing. This may also happen to the Visual Genome dataset. The region for the visual relation is the union of the head and the tail, which is larger than the two objects. The bigger union region may better correlate with the well-known centre bias regardless of what the actual content. This is why “action” is the most salient in all of the datasets in Figure 3.\n\nOverall, applying basis decomposition [42] is not new, the finding of the semantic importance given saliency may simply showing dataset biases.\nGeirhos, Robert, et al. \"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.\" arXiv preprint arXiv:1811.12231 (2018).\n\nQUESTIONS:\nSee above\n\nLIMITATIONS:\nExploring unknown bases to a dataset is questionable, which requires further study. The finding of those bases may not explain saliency, but the biases of the training and probing datasets.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper examines the problem of predicting visual saliency in images. Unlike many other works, it focuses on determining what leads to the predictions made, including underlying features that are learned, and formulating the prediction as a combination of bases. Through this, one is able to garner an understanding of how the models make their decisions and attach this to semantic information or other concepts.\n\nSTRENGTHS:\nStrengths of this paper are as follows:\n1. It approaches saliency from a different point of view; rather than competing to marginally beat an ROC score, it takes a step towards truly understanding the success of deep learning models in this domain\n2. By explicitly modelling features correlated with attention, and those inversely correlated with attention, one can model both what is salient and what is not in a way that allows these concepts to be quantified\n3. Because the model constructs it's prediction from a set of bases, this effectively allows human attention to be modelled subject to a change to these bases, as demonstrated in an example involving autism.\n\nWEAKNESSES:\nUltimately, it would be nice to see some quantitative results on how well the model performs compared to some of the state-of-the-art models, although I understand this is not the point of the paper. Nevertheless, a detailed set of results along this dimension could add value but I wouldn't insist on it.\n\nQUESTIONS:\n1. If possible, can you comment on how this formalism for saliency prediction extends to other domains?\n\n2. In figure 5, it seems that a single epoch of fine tuning is closer to careful fine tuning than the original weights. Is this from using a relatively large learning rate?\n\n3. Related to the above, how does this vary with changes to the learning rate?\n\nLIMITATIONS:\nThe authors have addressed limitations of their approach in Section 5 of the paper.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper attempts to decompose the learned representation of a data-driven saliency model into a constituent set of bases that are mapped onto semantic concepts, thereby providing insight into what is driving the model's representation of saliency. This method is applied to three different saliency models of varying formulation over several datasets. Some discussion is then provided of the results, as well as some qualitative discussion of examples of model failures.\n\nSTRENGTHS:\nThe paper takes on a challenging and open-ended problem in saliency modelling, namely the difficulty of teasing apart the different contributions to attentional capture. Similarly, the work provides an example of continued work in general explainability within deep learning methods, which is an important issue within the field.\n\nThe paper is clearly written and relatively easy to follow, although there are a few details that are missing (such as the choice of N).\n\nWEAKNESSES:\nThere are some references that seem pertinent that were not discussed. In particular, significant work on failure modes of modern saliency models was poorly represented.\n- Bruce et al., \"A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond\", CVPR 2016\n  -- This paper digs into some of the failure modes common to deep learning models, including object vs. background and semantic vs. feature contrast elements (e.g. see Figure 7), providing pertinent insights to the discussion in the submission.\n- Kümmerer et al., \"Understanding Low- and High-Level Contributions to Fixation Prediction\", ICCV 2017\n  -- This paper explicitly explores constituent elements of saliency representation in deep networks from the perspective of high-level vs. low-level features. Given the way the paper attempts to tease apart the representation of saliency between different feature classes, it is conceptually highly relevant background for the current submission.\n- Kotseruba et al., \"Do Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations\", BMVC 2019\n  -- This paper provides a psychophysical (P^3) and natural image (O^3) dataset with targets explicitly defined by low-level salient features (e.g. colour, orientation, shape, or size singletons), and finds that saliency models (including deep learning-based models) largely perform quite poorly. For exploring failure rates the O^3 dataset would be a potentially useful (albeit ground truth was defined by semantic object annotation and not fixation data), but even if the dataset is not used the examination of model failures in the submission should include the context of this prior exploration.\n- Tatler et al., \"Visual correlates of fixation selection: effects of scale and time\", Vision Research, 2005\n  -- This paper explores the evolution of fixations through time, including aspects such as central vs. peripheral distribution and inter-subject consistency of fixation location. Given that this is one area that the submission claims novelty, it would be good to put it in context with prior explorations in this area of the temporal evolution of low-level human attention.\n\nOverall, while the paper is interesting and tackles an exceedingly challenging problem, I think there are a number of conceptual issues that it needs to overcome. While some specific issues are given in the questions below, the primary issue is that while the submission encodes the positive/negative importance of the various bases extracted, it is well established within psychophysics that the relative importance of elements to saliency is contextual (e.g. see Nothdurft (1993) \"Saliency effects across dimensions in visual search\"; Nothdurft (2000) \"Salience from feature contrast: additivity across dimensions\" for some low level examples), and so these relative attributes are likely to change from image to image. Within the current submission, these attributes change from model to model and dataset to dataset; what conclusions are to be drawn from this? Is the technique shedding light on dataset composition, model bias, or some deeper aspect of relative aspects of saliency? Much of the analysis is presented without clear connection to either human behaviour (with the exception of Section 4.5, which cleverly makes use of the technique to explore the representations learned from data from different human subject populations or conditions) or model performance in a traditional sense, making it difficult to put into context or derive deeper insight.\n\nQUESTIONS:\n- Although drawing on the architectures of SALICON, DINet, and TranSalNet, the model configurations in this paper are distinct from the instantiations of the original publications in order to accommodate the need for trainable bases. How does that change the ultimate behaviour of the models with respect to standard measures of saliency performance? Alternatively, even a quantified value for the change in saliency maps when compared within model (e.g. the paper's version of SALICON correlated against the standard instantiation of SALICON) would help put the paper's results in context with the existing literature.\n\n- What is the value of N (the number of bases)? How stable are the results with respect to N (i.e. does the mapped semantic content change substantially with even small changes in N)?\n\n- Each basis is mapped onto a top-5 semantic mix. Why not onto a single concept? How was 5 selected? Similar to the previous question, how does this choice affect the subsequent analysis?\n\n- How is \"action\" defined in a static image? The primary example given, \"having meeting\", seems like a social activity, which was a separate category. I get that it is challenging to relate the messy details of semantic categorization in a short paper, but given that this is central to the topic of the paper, I think it needs a clearer explanation.\n\n- How do the insights provided in this paper relate to model performance? When the models show a markedly different breakdown of salient factors (e.g. Figure 4, which shows SALICON emphasizing vehicles much more strongly than TranSalNet, while TranSalNet emphasizes clothing more than any other model), does this correlate with predictive accuracy?\n\n- Related to the previous question, could you use the IoU Measurement process used to assign labels to the bases to provide an approximate breakdown of the factors leading to human fixations directly? This might provide another point of comparison to better put the results of this paper in context.\n\n- Given the range of behaviours across the models shown in Figure 4, why does Figure 5 average the semantic weights across models (also, this should be noted in the caption; when I first read the paper I was quite confused which model was being shown in Figure 5)? What is the justification for this? Do the models tend to converge after fine-tuning?\n\n- Figure 6 (b.) and (c.): are these results for subjects without autism? If so, this should be more clearly noted.\n\n- What was meant by line 304: \"Whether models... open question.\"? What would lead to a model behaving \"even better than humans\", given that humans are the system trying to be modelled?\n\nLIMITATIONS:\nThe limitations discussed seem clear.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a novel analytic framework that provides a principled interpretation and quantification of the implicit features learned by deep saliency models, which are used for predicting human visual attention. The framework decomposes these features into interpretable bases aligned with semantic attributes, and reformulates saliency prediction as a weighted combination of probability maps. The authors conducted extensive analyses to understand the factors contributing to the success of saliency models, including the positive and negative weights of semantics, the impact of training data and architectural designs, and the effects of fine-tuning. They also explored visual attention in various application scenarios, such as autism spectrum disorder, emotion-eliciting stimuli, and attention evolution over time. The study identifies the accurate feature detection and differentiation of semantics as key factors in the models' success, influenced by training data and design choices. The framework is also useful for characterizing human visual attention and understanding common failure patterns in saliency models. The authors suggest incorporating structures and lower-level information for improved modeling. The research has potential impacts on optimizing human-computer interfaces, assisting visually impaired individuals, and enhancing societal benefits.\n\nSTRENGTHS:\n1. The paper presents a novel analytic framework that provides interpretation and quantification of the implicit features learned by deep saliency models for predicting human visual attention. The framework decomposes implicit features into interpretable bases aligned with semantic attributes, allowing for a weighted combination of probability maps connecting the bases and saliency in saliency prediction. The framework effectively identifies a variety of semantics learned by deep saliency models, including social cues, actions, clothing, and salient object categories, showcasing its versatility in analyzing attention across diverse scenarios.\n2. The framework reveals the positive and negative contributions of semantics to saliency, highlighting the ability of deep saliency models to distinguish between salient and non-salient semantics.\n3. The analysis demonstrates how training data and model designs impact saliency prediction, with shifts in semantic weights reflecting the characteristics of the datasets and models.\n4. The study also investigates the effects of fine-tuning on semantic weights, showing how deep saliency models progressively adapt features during training to better capture salient cues and refine the weights of negative semantics.\n5. The framework is also applied to explore the capture of human attention characteristics, such as the impact of visual preferences, characteristics of visual stimuli (e.g., emotions), and temporal dynamics, providing insights into the factors influencing attention deployment.\n6. The findings validate the effectiveness of deep saliency models in automatically identifying salient semantics, differentiating foreground from background, and encoding fine-grained characteristics of attention.\n\nWEAKNESSES:\nThe work has a lot of novelty with good empirical analyses. The paper is also nicely written. However, I find that the authors have missed comparing the performance of their model with other saliency models using standard saliency metrics as can be found in this leaderboard: http://saliency.mit.edu/results_mit300.html.\n\nThe authors have also failed to cite some important works from like DeepGaze, DeepFix, BMS, etc.\n\nQUESTIONS:\nI am curious to know if the authors could perform and share the results of comparison with existing methods using the standard metrics for measuring saliency.\n\nLIMITATIONS:\nThe authors analyzed the failure patterns of deep saliency models within the intermediate inference process using their proposed factorization framework. They select common success and failure examples where three tested models consistently have high/low NSS scores. They then perform a qualitative analysis by visualizing the spatial probabilistic distribution of the bases for semantics with positive and negative weights.\n\nIn the successful examples, they find that accurate saliency prediction is correlated with the differentiation of diverse semantics. The stimuli in these examples typically have salient and non-salient regions belonging to different semantics. Therefore, the models, with the ability to distinguish positive and negative semantics, can readily determine the saliency distribution. On the other hand, in the failure examples, the models struggle to determine saliency within objects or among objects with similar semantics. Investigation of the probabilistic distribution of bases reveals that models often have a uniform-like distribution of bases on object parts or among objects of the same category, making it difficult to construct accurate saliency maps.\n\nThe analysis also highlights that existing models have difficulty with scenes without salient objects. These scenes are challenging for the models as they lack clear focal points. The ground truth human attention in these failure patterns exhibits high inter-subject variability, suggesting that human viewers may not agree on where to look, making the ground truth maps less reliable. The authors raise the question of whether models may perform as well as or even better than humans in these challenging situations.\n\nBased on their observations, the authors hypothesize that leveraging more structured representations to encode contextual relationships between semantics and integrating mid- and low-level cues may be beneficial in addressing these failure patterns in certain scenarios. This suggests that incorporating additional information and incorporating contextual understanding may help improve the performance of deep saliency models.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-07"}