A Dilated Inception Network for Visual Saliency Prediction

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.Comment: Accepted by IEEE Transactions on Multimedia. The source codes are available at https://github.com/ysyscool/DINe

Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace

To explain "black-box" properties of AI models, many approaches, such as post hoc and intrinsically interpretable models, have been proposed to provide plausible explanations that identify human-understandable features/concepts that a trained model uses to make predictions, and attention mechanisms have been widely used to aid in model interpretability by visualizing that information. However, the problem of configuring an interpretable model that effectively communicates and coordinates among computational modules has received less attention. A recently proposed shared global workspace theory demonstrated that networks of distributed modules can benefit from sharing information with a bandwidth-limited working memory because the communication constraints encourage specialization, compositionality, and synchronization among the modules. Inspired by this, we consider how such shared working memories can be realized to build intrinsically interpretable models with better interpretability and performance. Toward this end, we propose Concept-Centric Transformers, a simple yet effective configuration of the shared global workspace for interpretability consisting of: i) an object-centric-based architecture for extracting semantic concepts from input features, ii) a cross-attention mechanism between the learned concept and input embeddings, and iii) standard classification and additional explanation losses to allow human analysts to directly assess an explanation for the model's classification reasoning. We test our approach against other existing concept-based methods on classification tasks for various datasets, including CIFAR100 (super-classes), CUB-200-2011 (bird species), and ImageNet, and we show that our model achieves better classification accuracy than all selected methods across all problems but also generates more consistent concept-based explanations of classification output.Comment: 21 pages, 9 tables, 13 figure

Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation

Not all eye-tracking methodology and data processing are equal. While the use of eye-tracking is intricate because of its grounding in visual physiology, traditional 2D eye-tracking methods are supported by software, tools, and reference studies. This is not so true for eye-tracking methods applied in virtual reality (imaginary 3D environments). Previous research regarded the domain of eye-tracking in 3D virtual reality as an untamed realm with unaddressed issues. The present paper explores these issues, discusses possible solutions at a theoretical level, and offers example implementations. The paper also proposes a workflow and software architecture that encompasses an entire experimental scenario, including virtual scene preparation and operationalization of visual stimuli, experimental data collection and considerations for ambiguous visual stimuli, post-hoc data correction, data aggregation, and visualization. The paper is accompanied by examples of eye-tracking data collection and evaluation based on ongoing research of indoor evacuation behavior

Robustness and Interpretability of Neural Networks’ Predictions under Adversarial Attacks

Le reti neurali profonde (DNNs) sono potenti modelli predittivi, che superano le capacità umane in una varietà di task. Imparano sistemi decisionali complessi e flessibili dai dati a disposizione e raggiungono prestazioni eccezionali in molteplici campi di apprendimento automatico, dalle applicazioni dell'intelligenza artificiale, come il riconoscimento di immagini, parole e testi, alle scienze più tradizionali, tra cui medicina, fisica e biologia. Nonostante i risultati eccezionali, le prestazioni elevate e l’alta precisione predittiva non sono sufficienti per le applicazioni nel mondo reale, specialmente in ambienti critici per la sicurezza, dove l'utilizzo dei DNNs è fortemente limitato dalla loro natura black-box. Vi è una crescente necessità di comprendere come vengono eseguite le predizioni, fornire stime di incertezza, garantire robustezza agli attacchi avversari e prevenire comportamenti indesiderati. Anche le migliori architetture sono vulnerabili a piccole perturbazioni nei dati di input, note come attacchi avversari: manipolazioni malevole degli input che sono percettivamente indistinguibili dai campioni originali ma sono in grado di ingannare il modello in predizioni errate. In questo lavoro, dimostriamo che tale fragilità è correlata alla geometria del manifold dei dati ed è quindi probabile che sia una caratteristica intrinseca delle predizioni dei DNNs. Questa condizione suggerisce una possibile direzione al fine di ottenere robustezza agli attacchi: studiamo la geometria degli attacchi avversari nel limite di un numero infinito di dati e di pesi per le reti neurali Bayesiane, dimostrando che, in questo limite, sono immuni agli attacchi avversari gradient-based. Inoltre, proponiamo alcune tecniche di training per migliorare la robustezza delle architetture deterministiche. In particolare, osserviamo sperimentalmente che ensembles di reti neurali addestrati su proiezioni casuali degli input originali in spazi basso-dimensionali sono più resistenti agli attacchi. Successivamente, ci concentriamo sul problema dell'interpretabilità delle predizioni delle reti nel contesto delle saliency-based explanations. Analizziamo la stabilità delle explanations soggette ad attacchi avversari e dimostriamo che, nel limite di un numero infinito di dati e di pesi, le interpretazioni Bayesiane sono più stabili di quelle fornite dalle reti deterministiche. Confermiamo questo comportamento in modo sperimentale nel regime di un numero finito di dati. Infine, introduciamo il concetto di attacco avversario alle sequenze di amminoacidi per protein Language Models (LM). I modelli di Deep Learning per la predizione della struttura delle proteine, come AlphaFold2, sfruttano le architetture Transformer e il loro meccanismo di attention per catturare le proprietà strutturali e funzionali delle sequenze di amminoacidi. Nonostante l'elevata precisione delle predizioni, perturbazioni biologicamente piccole delle sequenze di input, o anche mutazioni di un singolo amminoacido, possono portare a strutture 3D sostanzialmente diverse. Al contempo, i protein LMs sono insensibili alle mutazioni che inducono misfolding o disfunzione (ad esempio le missense mutations). In particolare, le predizioni delle coordinate 3D non rivelano l'effetto di unfolding indotto da queste mutazioni. Pertanto, esiste un'evidente incoerenza tra l'importanza biologica delle mutazioni e il conseguente cambiamento nella predizione strutturale. Ispirati da questo problema, introduciamo il concetto di perturbazione avversaria delle sequenze proteiche negli embedding continui dei protein LMs. Il nostro metodo utilizza i valori di attention per rilevare le posizioni degli amminoacidi più vulnerabili nelle sequenze di input. Le mutazioni avversarie sono biologicamente diverse dalle sequenze di riferimento e sono in grado di alterare in modo significativo le strutture 3D.Deep Neural Networks (DNNs) are powerful predictive models, exceeding human capabilities in a variety of tasks. They learn complex and flexible decision systems from the available data and achieve exceptional performances in multiple machine learning fields, spanning from applications in artificial intelligence, such as image, speech and text recognition, to the more traditional sciences, including medicine, physics and biology. Despite the outstanding achievements, high performance and high predictive accuracy are not sufficient for real-world applications, especially in safety-critical settings, where the usage of DNNs is severely limited by their black-box nature. There is an increasing need to understand how predictions are performed, to provide uncertainty estimates, to guarantee robustness to malicious attacks and to prevent unwanted behaviours. State-of-the-art DNNs are vulnerable to small perturbations in the input data, known as adversarial attacks: maliciously crafted manipulations of the inputs that are perceptually indistinguishable from the original samples but are capable of fooling the model into incorrect predictions. In this work, we prove that such brittleness is related to the geometry of the data manifold and is therefore likely to be an intrinsic feature of DNNs’ predictions. This negative condition suggests a possible direction to overcome such limitation: we study the geometry of adversarial attacks in the large-data, overparameterized limit for Bayesian Neural Networks and prove that, in this limit, they are immune to gradient-based adversarial attacks. Furthermore, we propose some training techniques to improve the adversarial robustness of deterministic architectures. In particular, we experimentally observe that ensembles of NNs trained on random projections of the original inputs into lower dimensional spaces are more resilient to the attacks. Next, we focus on the problem of interpretability of NNs’ predictions in the setting of saliency-based explanations. We analyze the stability of the explanations under adversarial attacks on the inputs and we prove that, in the large-data and overparameterized limit, Bayesian interpretations are more stable than those provided by deterministic networks. We validate this behaviour in multiple experimental settings in the finite data regime. Finally, we introduce the concept of adversarial perturbations of amino acid sequences for protein Language Models (LMs). Deep Learning models for protein structure prediction, such as AlphaFold2, leverage Transformer architectures and their attention mechanism to capture structural and functional properties of amino acid sequences. Despite the high accuracy of predictions, biologically small perturbations of the input sequences, or even single point mutations, can lead to substantially different 3d structures. On the other hand, protein language models are insensitive to mutations that induce misfolding or dysfunction (e.g. missense mutations). Precisely, predictions of the 3d coordinates do not reveal the structure-disruptive effect of these mutations. Therefore, there is an evident inconsistency between the biological importance of mutations and the resulting change in structural prediction. Inspired by this problem, we introduce the concept of adversarial perturbation of protein sequences in continuous embedding spaces of protein language models. Our method relies on attention scores to detect the most vulnerable amino acid positions in the input sequences. Adversarial mutations are biologically diverse from their references and are able to significantly alter the resulting 3D structures

A computational dynamical model of human visual cortex for visual search and feature-based attention

Visual attention can be deployed to locations within the visual array (spatial attention), to individual features such as colour and form (feature-based attention), or to entire objects (object-based attention). Objects are composed of features to form a perceived ‘whole’. This compositional object representation reduces the storage demands by avoiding the need to store every type of object experienced. However, this approach exposes a problem of binding these constituent features (e.g. form and colour) into objects. The problem is made explicit in the higher areas of the ventral stream as information about a feature’s location is absent. For feature-based attention and search, activations flow from the inferotemporal cortex to primary visual cortex without spatial cues from the dorsal stream, therefore the neural effect is applied to all locations across the visual field [79, 60, 7, 52]. My research hypothesis is that biased competition occurs independently for each cued feature, and is implemented by lateral inhibition between a feedforward and a feedback network through a cortical micro-circuit architecture. The local competition for each feature can be combined in the dorsal stream via spatial congruence to implement a secondary spatial attention mechanism, and in early visual areas to bind together the distributed featural representation of a target object

Towards Learning Representations in Visual Computing Tasks

abstract: The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos. The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss. In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Neural information extraction from natural language text

Natural language processing (NLP) deals with building computational techniques that allow computers to automatically analyze and meaningfully represent human language. With an exponential growth of data in this digital era, the advent of NLP-based systems has enabled us to easily access relevant information via a wide range of applications, such as web search engines, voice assistants, etc. To achieve it, a long-standing research for decades has been focusing on techniques at the intersection of NLP and machine learning. In recent years, deep learning techniques have exploited the expressive power of Artificial Neural Networks (ANNs) and achieved state-of-the-art performance in a wide range of NLP tasks. Being one of the vital properties, Deep Neural Networks (DNNs) can automatically extract complex features from the input data and thus, provide an alternative to the manual process of handcrafted feature engineering. Besides ANNs, Probabilistic Graphical Models (PGMs), a coupling of graph theory and probabilistic methods have the ability to describe causal structure between random variables of the system and capture a principled notion of uncertainty. Given the characteristics of DNNs and PGMs, they are advantageously combined to build powerful neural models in order to understand the underlying complexity of data. Traditional machine learning based NLP systems employed shallow computational methods (e.g., SVM or logistic regression) and relied on handcrafting features which is time-consuming, complex and often incomplete. However, deep learning and neural network based methods have recently shown superior results on various NLP tasks, such as machine translation, text classification, namedentity recognition, relation extraction, textual similarity, etc. These neural models can automatically extract an effective feature representation from training data. This dissertation focuses on two NLP tasks: relation extraction and topic modeling. The former aims at identifying semantic relationships between entities or nominals within a sentence or document. Successfully extracting the semantic relationships greatly contributes in building structured knowledge bases, useful in downstream NLP application areas of web search, question-answering, recommendation engines, etc. On other hand, the task of topic modeling aims at understanding the thematic structures underlying in a collection of documents. Topic modeling is a popular text-mining tool to automatically analyze a large collection of documents and understand topical semantics without actually reading them. In doing so, it generates word clusters (i.e., topics) and document representations useful in document understanding and information retrieval, respectively. Essentially, the tasks of relation extraction and topic modeling are built upon the quality of representations learned from text. In this dissertation, we have developed task-specific neural models for learning representations, coupled with relation extraction and topic modeling tasks in the realms of supervised and unsupervised machine learning paradigms, respectively. More specifically, we make the following contributions in developing neural models for NLP tasks: 1. Neural Relation Extraction: Firstly, we have proposed a novel recurrent neural network based architecture for table-filling in order to jointly perform entity and relation extraction within sentences. Then, we have further extended our scope of extracting relationships between entities across sentence boundaries, and presented a novel dependency-based neural network architecture. The two contributions lie in the supervised paradigm of machine learning. Moreover, we have contributed in building a robust relation extractor constrained by the lack of labeled data, where we have proposed a novel weakly-supervised bootstrapping technique. Given the contributions, we have further explored interpretability of the recurrent neural networks to explain their predictions for the relation extraction task. 2. Neural Topic Modeling: Besides the supervised neural architectures, we have also developed unsupervised neural models to learn meaningful document representations within topic modeling frameworks. Firstly, we have proposed a novel dynamic topic model that captures topics over time. Next, we have contributed in building static topic models without considering temporal dependencies, where we have presented neural topic modeling architectures that also exploit external knowledge, i.e., word embeddings to address data sparsity. Moreover, we have developed neural topic models that incorporate knowledge transfers using both the word embeddings and latent topics from many sources. Finally, we have shown improving neural topic modeling by introducing language structures (e.g., word ordering, local syntactic and semantic information, etc.) that deals with bag-of-words issues in traditional topic models. The class of proposed neural NLP models in this section are based on techniques at the intersection of PGMs, deep learning and ANNs. Here, the task of neural relation extraction employs neural networks to learn representations typically at the sentence level, without access to the broader document context. However, topic models have access to statistical information across documents. Therefore, we advantageously combine the two complementary learning paradigms in a neural composite model, consisting of a neural topic and a neural language model that enables us to jointly learn thematic structures in a document collection via the topic model, and word relations within a sentence via the language model. Overall, our research contributions in this dissertation extend NLP-based systems for relation extraction and topic modeling tasks with state-of-the-art performances