SlowGuess/NeurIPS_2023_OCR huggingface dataset:
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.
(b)
Figure 6: Semantic weights for characterizing the attention on three different settings. From left to right are results for the attention of people with and without autism, attention to stimuli eliciting different emotions, and the attention of different time periods. Figures share the same y-axis. Note that the results in the three figures are derived from different datasets selected based on the context of analyses.
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.
Figure 7: Successful (left) and failure (right) examples of deep saliency models. We visualize the predictions from DINet, as those for the other two models are similar. For the distribution of bases, from red to blue, the probabilities of positive bases decrease while those for negative bases increase.
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 ( $\alpha$  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
OSIE [17]	MIT [16]	SALICON [12]
NSS	CC	NSS	CC	NSS	CC
SALICON [9]	2.75	0.63	2.56	0.70	1.89	0.86
SAM [8]	2.70	0.65	2.47	0.69	1.84	0.86
DINet [11]	2.88	0.63	2.54	0.70	1.92	0.87
Ours	2.91	0.64	2.53	0.70	1.89	0.86
Table 1: Comparative results of saliency prediction on three popular datasets. Models are evaluated with two common metrics, including Normalized Scanpath Saliency (NSS) [49] and Correlation Coefficient (CC) [50].
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.
This work is supported by NSF Grants 2143197 and 2227450.
[45] Iuliia Kotseruba, Calden Wloka, Amir Rasouli, and John K. Tsotsos. Do saliency models detect odd-one-out targets? new datasets and evaluations. In BMVC, 2019.
[46] Matthias Kümmerer, Thomas S.A. Wallis, Leon A. Gatys, and Matthias Bethge. Understanding low- and high-level contributions to fixation prediction. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 4799–4808, 2017.
[47] Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba. Interpretable basis decomposition for visual explanation. In ECCV, pages 119-134, 2018.
[48] David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba. Network dissection: Quantifying interpretability of deep visual representations. In CVPR, pages 3319-3327, 2017.
[49] Robert J. Peters, Asha Iyer, Laurent Itti, and Christof Koch. Components of bottom-up gaze allocation in natural images. Vision Research, 45(18):2397 - 2416, 2005.
[50] Olivier Le Meur, Patrick Le Callet, and Dominique Barba. Predicting visual fixations on video based on low-level visual features. Vision Research, 47(19):2483 - 2498, 2007.
[51] Solomon Kullback. Information Theory and Statistics. Wiley, 1959.
[52] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255, 2009.
[53] Shuo Wang, Ming Jiang, Xavier Morin Duchesne, Elizabeth A. Laugeson, Daniel P. Kennedy, Ralph Adolphs, and Qi Zhao. Atypical visual saliency in autism spectrum disorder quantified through model-based eye tracking. Neuron, 88(3):604 - 616, 2015.
[54] Julie Osterling and Geraldine Dawson. Early recognition of children with autism: A study of first birthday home videotapes. Journal of Autism and Developmental Disorders, 24(3):247-257, 1994.
[55] Morton Ann Gernsbacher, Jennifer L Stevenson, Suraiya Khandakar, and H Hill Goldsmith. Why does joint attention look atypical in autism? Child Development Perspectives, 2(1):38-45, 2008.
[56] Rebecca J. Compton. The interface between emotion and attention: A review of evidence from psychology and neuroscience. Behavioral and Cognitive Neuroscience Reviews, 2(2):115-129, 2003.
[57] Patrik Vuilleumier. How brains beware: neural mechanisms of emotional attention. Trends in Cognitive Sciences, 9:585-594, 2005.