# Performance Evaluation of Neuromorphic Visual Attention Based on Proto-Objects

Amélie Gruel, Adrien F Vincent, Hong Chang, Sylvain Saïghi

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Amélie Gruel, Adrien F Vincent, Hong Chang, Sylvain Saïghi. Performance Evaluation of Neuromorphic Visual Attention Based on Proto-Objects. 2025 IEEE 7th International Conference on AI Circuits and Systems (AICAS), Apr 2025, Bordeaux, France. pp.1-5, 10.1109/AICAS64808.2025.11173121. hal-05344588

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# Performance Evaluation of Neuromorphic Visual Attention Based on Proto-Objects

Amélie Gruel*, Adrien F. Vincent*, Chip Hong Chang†‡, and Sylvain Saïghi*‡

* Laboratoire de l'Intégration du Matériau au Système, Univ. Bordeaux, Bordeaux INP, CNRS- F-33400 Talence, France

† School of Electrical and Electronic Engineering, Nanyang Technological University - Singapore

‡ CNRS@CREATE LTD, 1 Create Way, #08-01 CREATE Tower, Singapore 138602

Email: {amelie.gruel, pierre.lewden, adrien.vincent, sylvain.saighi}@u-bordeaux.fr*, echchang@ntu.edu.sg†

***Abstract***—Human beings detects saliency in a visual scene using different mechanisms experimentally studied over the years, such as the detection of “proto-objects”, i.e. salient regions in the visual scene where an object could plausibly be. Proto-object saliency-based models’ ability to be both stable and adaptable to any scenario has caught the eye of researchers from both traditional and neuromorphic computer vision, who have often applied bio-inspired visual attention mechanisms to optimise their model. We introduce *protoNVA*, a novel neuromorphic model for end-to-end saliency detection in event-based visual scenes, drawing inspiration from perceptual grouping mechanisms. Our simulation results show that this model is both more efficient in terms of memory usage and energy consumption, as well as outperforms the saliency detection realised by the state-of-the-art neuromorphic model for proto-objects detection. In addition, *protoNVA* achieves a better trade-off between architecture weight and saliency detection performance than existing neuromorphic models for visual attention, farther from biology and more suitable for hardware implementation.

***Index Terms***—neuromorphic, proto-objects, visual attention, computer vision

## I. INTRODUCTION

Over the years, visual attention in human beings has been widely studied in many experimental fields of science such as psychology, neurophysiology and biochemistry. This biological behaviour allows an organism to selectively focus on part of the visual information it receives, in order to reduce the amount of data to process, enhance the selected stimulus and ease object recognition [1]. With the increasing size of artificial sensors and the ever improving performance of artificial neural networks (ANNs), these three behavioural challenges gained relevance within the field of computer vision, which lead to the development and implementation of numerous computational models of visual attention [2], [3].

In conjunction with the significant development of artificial intelligence and computer vision, a number of technologies have emerged, often geared towards energy saving and low memory usage. Neuromorphic computing is one of those: introduced by Mead in the 1980s, this field of research

encompasses biology, electrical engineering, computer science and mathematics to develop ANNs and sensors mimicking information processing as performed by a human brain [4]. Spiking neural networks (SNNs) constitute a type of neuromorphic processing, implementing neural networks by more closely mimicking the biological neuron’s dynamics [5], [6]. This processing incorporates the time element in computing as well as allows for sparse computation, high connectivity, low computational frequency and, when deployed on compatible neuromorphic hardware, low power consumption [7]. Thanks to the event-based nature of its dynamics, SNNs make a particularly good match with a specific, low power and low memory visual neuromorphic sensor: the event-based camera [8], [9].

Neuromorphic computer vision faces the same limitations as traditional ANNs, particularly in terms of memory bandwidth [10], and recent works applied visual attention to overcome part of these challenges [11]–[17]. Indeed, limiting processing to relevant information while maintaining the quality of the information conveyed allows for an optimised system performance [18]. However, only a small fraction of these works are natively neuromorphic and take advantage of the uniqueness of SNNs (see [18], [19]).

In early 2000s, R.A. Rensink published his *coherency theory* where he highlights the uncanny ability of the human brain to serially attend objects before they are recognised, i.e. to address in priority regions in the visual scene that plausibly correspond to an object. This mechanism is particularly noteworthy in that it simultaneously considers top-down and bottom-up attention leading to better stability in visual tracking [20]. These regions, often characterised by closed boundaries, uniform colour or greatly distinct features, were named *proto-objects* [21], [22].

The concept of proto-object struck a chord among the computer vision community: many models of visual attention based on proto-objects have been deployed in recent years, drawing inspiration from biology and the primate visual system [20], [23]–[30]. A first application to neuromorphic computing was proposed by Iacono et al. [31], who adapted [25] to event-based data; this work was extended by Ghosh et al. [32], who drew inspiration from [29] and added depth estimation within proto-object detection in event data. [33] introduces the first SNN model for proto-object detection on

This work is supported by a public grant overseen by the French National Research Agency (ANR) as part of the ‘Chaires IA’ programme (GrAI project ANR-19-CHIA-0003) and as part of the ‘PEPR IA France 2030’ programme (Emergences project ANR-23-PEIA-0002). This research is part of the programme DesCartes and is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
<table><caption>TABLE I: Qualitative comparison between human fixations and saliency detected by SNNevProto, SNNevSTA and protoNVA.</caption><thead><tr><th>#</th><th>Input</th><th>Human</th><th>SNNevProto</th><th>SNNevSTA (1P, WTA)</th><th>protoNVA (D, ØI)</th></tr></thead><tbody><tr><td>6</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

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RGB data. Finally, D'Angelo et al. [17] were the first to propose an end-to-end neuromorphic proto-object detection by implementing Iacono's work using SNNs on SpiNNaker [34].

In this work, we aim to develop a neuromorphic model that would be easily deployable on neuromorphic hardware, performing saliency detection and drawing from biological concepts. We introduce protoNVA, a novel SNN model for event-based visual attention based on proto-objects. We take inspiration from both [17], for its state-of-the-art approach to neuromorphic proto-object detection, and [16], for its efficient exploitation of neuromorphic dynamics. Additionally, we propose to compare the energy efficiency and memory usage of protoNVA, [17] and [16]. Finally, we evaluate the performance of these three models using benchmark metrics for saliency detection in relation to existing human fixation maps.

## II. METHODS

In this work, we identify two end-to-end neuromorphic approaches to saliency detection within the state-of-the-art, to compare to protoNVA, using metrics from traditional computer vision. The output of each approach and associated variants are accumulated into saliency maps that are then compared to experimental human eye fixation maps [35] (see Tab. I).

### A. State-of-the-art

1) **SNNevSTA**: The first approach tackles neuromorphic bottom-up, covert attention using a two-layer SNN model with no overlap, relying on intrinsic SNN dynamics and adaptive weights [16], [36]. In the remainder of this paper, this method will be referred to as **SNNevSTA** (for "SNN performing event-based spatio-temporal attention"). In this work, we derived this model into four variants to compare the output saliency maps: with (2P) or without (1P) the polarities split in input; with (WTA) or without (ØWTA) a winner-take-all (WTA) mechanism on the output.

2) **SNNevProto**: To the best of our knowledge, the second approach is the first to perform an end-to-end neuromorphic proto-object detection, i.e. implemented with SNNs and applied to event data [17]. This model known as **SNNevProto** draws inspiration from biology: it extends to SNNs the model for event-based proto-object detection from [31], inspired by [25]. The authors deploy their network on SpiNNaker and provide various experimental results, such as numerical architectural details, spiking activity and performance of saliency detection compared to human performance.

### B. Our neuromorphic model: protoNVA

The protoNVA model introduced in this work aims to implement the neuromorphic detection of approximated proto-objects in an event-based scene, drawing inspiration from

both [17] and [16]. This model relies on the idea that an event-based proto-object is akin to an object with closed borders, thus it can be delimited by detecting uninterrupted activation between close neighbours. As event data is asynchronous, one can approximate such a behaviour by detecting relatively closed pixel activations (i.e. events) in time and space, making good use of the leaky behaviour of SNNs (similarly to [16]).

As depicted in Fig. 1, protoNVA comprises one input and one output layers of the same size. Each neuron is assimilated to the corresponding pixel of an event camera. The output layer is constituted of Leaky-Integrate-and-Fire (LIF) neurons [6]. Centred on each input neuron, is applied a filter connecting this input neuron and its surrounding neighbours to the output neuron at the same index. The neighbouring input neurons situated on the vertical or horizontal lines spanning from the central input neuron positively activate the output neuron. A first variant of this model, noted as *D* (or *ØD*) in hereinafter, includes diagonal activations: the output neuron is additionally excited by the events output by neighbouring input neurons situated on the four diagonal lines spanning from the central input neuron. A second variation, noted as *I* (or *ØI*), includes a regulatory inhibition: a second filter, similar to the first one in size and position, inhibits the output neuron proportionally to the activity in all neighbouring input neurons. This second filter helps to reduce the impact of noise, as we wish to detect borders (i.e. lines) and not a large, concentrated activity. The inhibition weight is strictly inferior to the activation weight to limit the regulation and allow the model to spike. The activation weight is the same among all excitatory synapses, and likewise for the inhibitory synapses. Similarly to [17], one can modify the overlay between two successive filters to limit architecture complexity and memory use. This synaptic pattern is similar to a convolution with varying stride (equal to 1 for an overlay of 100%) and with a specialised binary kernel.

The filter size, equal to (*pattern_length* × 2 + 1)², depends on the length of the pattern one wish to detect, proportionally

<figure><img src="image_7.png" /><figcaption>Fig. 1: Architecture of our protoNVA model. Green squares show pixels positively connected to the corresponding output pixel; likewise, red indicates inhibition. White squares denote the absence of synapses. The green diagonals and the inhibition filter are optional.</figcaption></figure>
<table><caption>TABLE II: SNN parameters used to implement protoNVA and SNNevSTA, according to AUC-Judd metric.</caption><thead><tr><th>protoNVA</th><th>D, I</th><th>ØD, I</th><th>D, ØI</th><th>ØD, ØI</th></tr></thead><tbody><tr><td>activation weight</td><td>1</td><td>1</td><td>0.5</td><td>0.5</td></tr><tr><td>inhibition weight</td><td>0.5</td><td>0.5</td><td>/</td><td>/</td></tr><tr><td>overlay</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td>pattern length</td><td>1</td><td>1</td><td>20</td><td>20</td></tr><tr><th>SNNevSTA</th><th>2P, WTA</th><th>1P, WTA</th><th>2P, ØWTA</th><th>1P, ØWTA</th></tr><tr><td>downscaling factor</td><td>20</td><td>20</td><td>5</td><td>5</td></tr><tr><td>activation weight</td><td>0.5</td><td>0.5</td><td>0.5</td><td>0.5</td></tr><tr><td>WTA max. weight</td><td>1</td><td>1</td><td>/</td><td>/</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>Fig. 2: Comparison between the number of neurons (top) and synapses (bottom) required by SNNevProto and different variants of SNNevSTA and protoNVA, for an input sensor of size (160pix, 120pix). The values vary according to the downscaling factor (top label) for SNNevSTA, and to the overlay (bottom label) for SNNevProto and protoNVA. The transparent areas depict the variation extent of protoNVA values according to the chosen pattern length. Of note: overlapping SNNevSTA markers are shifted two-by-two on the x-axis to ease visualisation. Top figure represents both number of neurons in SNNevProto as provided in [17] (dotted line) and as computed according to their architecture description (full line).</figcaption></figure>

related to the potential proto-objects' size. Additionally, the size and settings of the event camera used will influence the model performance in relation with the filter size: for example a small camera will output events from spatially closer pixels; a higher contrast threshold will lead to fewer events altogether, thus a smaller probability of spatially close events.

Similarly to most state-of-the-art approaches, protoNVA does not require any learning. For a given dataset, one can

manually tune the network's parameters upstream, such as the weights, pattern length, overlay, etc. (see Tab. II).

### C. Synthetic dataset

As it is often the case in neuromorphic computing, natively event-driven datasets addressing the salience detection task are not yet publicly available to the best of our knowledge. State-of-the-art approaches either produce synthetic datasets from existing RGB databases for saliency detection [17], [31], [32], make use of existing event-based datasets [14]–[16], [36], or record custom-made datasets [17], [32] – the two latter solutions suffering from the absence of corresponding fixation maps (human or from state-of-the-art) as baseline. As we wish to make use of benchmark metrics allowing for comparison with a baseline fixation map, ideally recorded from human behaviour, we opted for the first approach.

The NUS3D-Saliency dataset consists of 600 RGB images and corresponding eye fixation maps recorded from 80 participants facing these colour stimuli [35]. We randomly selected 100 samples as in [17], resized them to (160,120) to facilitate simulations and then translated them into events according to the protocol established in [32].

## III. SIMULATION RESULTS

The results below were obtained on CPU using the NEST simulator [37] interfaced with PyNN [38]. A first phase of manual tuning for each variant was performed to obtain the parameters used here (see Tab. II). For each set, the performance was arbitrarily asserted using the AUC-Judd metric [39].

### A. Study of architectures

Generally speaking, SNNs with a lighter architecture (i.e. a smaller number of neurons and synapses) are easier to deploy on neuromorphic hardware due to resource limitations. Indeed, limiting the number of parameters, itself directly proportional to the number of neurons and synapses (whether the parameters are shared or not), intrinsically limits the memory usage. The energy consumption of most neuromorphic architectures is also proportional to the number of synaptic operations among others [40] – reducing the number of synapses within a model will thus lead to a reduced energy consumption.

As we aim to develop a neuromorphic visual attention model based on proto-objects and easily deployable on neuromorphic hardware, we study the resources used by protoNVA in comparison with SNNevProto and SNNevSTA in Fig. 2. It is legitimate to compare the number of synapses and neurons instead of the number of parameters since all three models implement synapses and neurons with shared parameters. According to Fig. 2(top), SNNevSTA has a significantly smaller number of neurons (increasing with a decreasing downscaling factor). Among the proto-object-based approaches, most of the time our protoNVA requires less neurons to be simulated than SNNevProto; indeed, even though the output layer is of same size as the input, an overlay inferior to 100% leads to synaptic patterns that connect only a fraction of output neurons. This number of activated output neurons increases for smaller
<figure><img src="image_9.png" /><figcaption>Fig. 3: Saliency detection performance of SNNevProto, SNNevSTA and protoNVA on NUS3D compared to human fixation maps. SNNevProto results come from [17]. The parameters used to obtain the results above for each variant of SNNevSTA and protoNVA were obtained after a manual tuning phase on NUS3D (see Tab. II). The dotted grey lines indicate the best and worse performance of SNNevProto.</figcaption></figure>

pattern lengths, leading to a smaller set of possible overlays. However, contrarily to the number of neurons, Fig. 2(bottom) shows that SNNevSTA requires significantly more synapses, even without the WTA (ØWTA), and is only outnumbered by SNNevProto at an 100% overlay. Variants of protoNVA including diagonals (D) require on average as many synapses as SNNevProto, the latter increasing the distance upwards for an overlay greater than 70%; variants of protoNVA excluding diagonals (ØD) are below SNNevProto.

This study shows that out of all three models, SNNevSTA's architecture is better in terms of neurons, but worse in terms of synapses. Depending on the variants, the architecture of our protoNVA is as easily deployable on neuromorphic hardware as, if not more so than, SNNevProto. To be noted that SNNevSTA implements dynamic weight adaptation rules, which are not supported by many neuromorphic hardware.

## B. Comparison to human visual attention

Fig. 3 compares the outputs of SNNevProto, SNNevSTA and protoNVA on a subset of NUS3D-Saliency to corresponding human fixations using five benchmark performance metrics for saliency detection, namely the Judd and Borji variants of the Area under the ROC Curve (AUC-Judd and AUC-Borji), the normalised scanpath saliency (NSS), the similarity (SIM) and the structural similarity index (SSIM). AUCs, NSS and SIM are implemented following [41]–[46]; SSIM is computed using [47]. Of note: saliency metrics vary greatly from one another, and it remains a challenge to “objectively determine [...] the best approximation to human eye fixations [due to] the inconsistency in how different metrics rank saliency models” [45]. Our choice of saliency metrics was motivated both by the selection made in [17] and the complementary review of existing saliency metrics presented in [39] and [45]. For all metrics, the closer to 1 the better.

Fig. 3 confirms the observations of [17]: each saliency metric judges a different aspect of similarity between human and SNNs fixations maps, which leads to different behaviours depending on the selected metric. For AUC-Judd, AUC-Borji, NSS and SIM, the protoNVA variants without inhibition

outperforms the SNNevProto and SNNevSTA models, while the SSIM performance of SNNevSTA variants with WTA is greater than both SNNevProto and protoNVA. These results show that leveraging SNN intrinsic dynamics allows SNNevSTA and protoNVA to perform better saliency detection. However, the metrics considered here do not allow for definitive selection between different variants of SNNevSTA or SNNevProto: adding WTA to the SNNevSTA increases the performance as measured by AUC-Judd, NSS and SSIM but decreases the metrics AUC-Borji and SIM; similarly, adding diagonals within the synaptic activation pattern of protoNVA seems to slightly increase the metrics AUC-Judd, AUC-Borji, NSS and SIM but reduces the SSIM more sharply. Taking the memory usage and energy consumption of the model architecture into consideration can better justify and facilitate the choice of an optimal neuromorphic model for end-to-end saliency detection: our protoNVA model, and particularly its variants without inhibition, offers the best trade-off between performance and architecture complexity.

## IV. CONCLUSION

We introduce protoNVA, a novel SNN model of visual attention performing end-to-end neuromorphic saliency detection on event-based visual scenes, based on the biological concept of pseudo-objects and with no learning phase. Our model requires less neurons and synapses for a similar size of input data than SNNevProto [17], the state-of-the-art neuromorphic approach for proto-object detection, facilitating its deployment on neuromorphic hardware with reduced memory usage and energy consumption. It additionally outperforms SNNevProto detection on most saliency metrics compared to baseline human fixations. Finally, protoNVA achieves a better trade-off between architecture weight and saliency detection performance than SNNevSTA [16], a neuromorphic visual attention model farther from biology.

In future works, we wish to investigate additional datasets of different resolutions for saliency detection, such as CAT2000 [46] or the CRCNS dataset [48]. We also aim at hardware implementation, for example on SpiNNaker [34].
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