Reviewerly/exHarmony huggingface dataset:
Interpreting Disparate Privacy-Utility Tradeoff in Adversarial Learning via Attribute Correlation

Adversarial learning is commonly used to extract latent data representations which are expressive to predict the target attribute but indistinguishable in the privacy attribute. However, whether they can achieve an expected privacy-utility tradeoff is of great uncertainty. In this paper, we posit it is the complex interaction between different attributes in the training set that causes disparate tradeoff results. We first formulate the measurement of utility, privacy and their tradeoff in adversarial learning. Then we propose the metrics of Statistical Reliability (SR) and Feature Reliability (FR) to quantify the relationship between attributes. Specifically, SR reflects the co-occurrence sampling bias of the joint distribution between two attributes. Beyond the explicit dependence, FR exploits the intrinsic interaction one attribute exerts on the other via exploring the representation disentanglement. We validate the metrics on CelebA and LFW dataset with a suite of target-privacy attribute pairs. Experimental results demonstrate the strong correlations between the metrics and utility, privacy and their tradeoff. We further conclude how to use SR and FR as a guide to the setting of the privacy-utility tradeoff parameter.
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search

In this work, we propose a novel and scalable solution to address the challenges of developing efficient dense predictions on edge platforms. Our first key insight is that MultiTask Learning (MTL) and hardware-aware Neural Architecture Search (NAS) can work in synergy to greatly benefit on-device Dense Predictions (DP). Empirical results reveal that the joint learning of the two paradigms is surprisingly effective at improving DP accuracy, achieving superior performance over both the transfer learning of single-task NAS and prior state-of-the-art approaches in MTL, all with just 1/10th of the computation. To the best of our knowledge, our framework, named EDNAS, is the first to successfully leverage the synergistic relationship of NAS and MTL for DP. Our second key insight is that the standard depth training for multi-task DP can cause significant instability and noise to MTL evaluation. Instead, we propose JAReD, an improved, easy-to-adopt Joint Absolute-Relative Depth loss, that reduces up to 88% of the undesired noise while simultaneously boosting accuracy. We conduct extensive evaluations on standard datasets, benchmark against strong baselines and state-of-the-art approaches, as well as provide an analysis of the discovered optimal architectures.
LAB: Learnable Activation Binarizer for Binary Neural Networks

Binary Neural Networks (BNNs) are receiving an up-surge of attention for bringing power-hungry deep learning towards edge devices. The traditional wisdom in this space is to employ sign(.) for binarizing feature maps. We argue and illustrate that sign(.) is a uniqueness bottleneck, limiting information propagation throughout the network. To alleviate this, we propose to dispense sign(.), replacing it with a learnable activation binarizer (LAB), allowing the network to learn a fine-grained binarization kernel per layer - as opposed to global thresholding. LAB is a novel universal module that can seamlessly be integrated into existing architectures. To confirm this, we plug it into four seminal BNNs and show a considerable accuracy boost at the cost of tolerable increase in delay and complexity. Finally, we build an end-to-end BNN (coined as LAB-BNN) around LAB, and demonstrate that it achieves competitive performance on par with the state-of-the-art on ImageNet. Our code can be found in our repository: https://github.com/sfalkena/LAB.
Stop or Forward: Dynamic Layer Skipping for Efficient Action Recognition

One of the challenges for analyzing video contents (e.g., actions) is high computational cost, especially for the tasks that require processing densely sampled frames in a long video. We present a novel efficient action recognition algorithm, which allocates computational resources adaptively to individual frames depending on their relevance and significance. Specifically, our algorithm adopts LSTM-based policy modules and sequentially estimates the usefulness of each frame based on their intermediate representations. If a certain frame is unlikely to be helpful for recognizing actions, our model stops forwarding the features to the rest of the layers and starts to consider the next sampled frame. We further reduce the computational cost of our approach by introducing a simple yet effective early termination strategy during the inference procedure. We evaluate the proposed algorithm on three public benchmarks: ActivityNet-v1.3, Mini-Kinetics, and THUMOS’14. Our experiments show that the proposed approach achieves outstanding trade-off between accuracy and efficiency in action recognition.
Sim2RealVS: A New Benchmark for Video Stabilization with a Strong Baseline

Video stabilization is highly desirable when videos undergo severe jittering artifacts. The difficulty of obtaining sufficient training data obstructs the development of video stabilization. In this work, we address this issue by presenting a Sim2RealVS benchmark with more than 1,300 pairs of shaky and stable videos. Our benchmark is curated by an in-game simulator with diverse scenes and various jittering effects. Moreover, we propose a simple yet strong baseline approach, named Motion-Trajectory Smoothing Network (MTSNet), by fully exploiting our Sim2RealVS data. Our MTSNet consists of three main steps: motion estimation, global trajectory smoothing and frame warping. In motion estimation, we design a Motion Correction and Completion (MCC) module to rectify the optical flow with low confidence, such as in textureless regions, thus providing more consistent motion estimation for next steps. Benefiting from our synthetic data, we can explicitly learn a Trajectory Smoothing Transformer (TST) with ground-truth supervision to smooth global trajectories. In training TST, we propose two fully-supervised losses, i.e., a motion magnitude similarity loss and a motion tendency similarity loss. After training, our TST is able to produce smooth motion trajectories for the shaky input videos. Extensive qualitative and quantitative results demonstrate that our MTSNet achieves superior performance on both synthetic and real-world data.
Improving saliency models’ predictions of the next fixation with humans’ intrinsic cost of gaze shifts

The human prioritization of image regions can be modeled in a time invariant fashion with saliency maps or sequentially with scanpath models. However, while both types of models have steadily improved on several benchmarks and datasets, there is still a considerable gap in predicting human gaze. Here, we leverage two recent developments to reduce this gap: theoretical analyses establishing a principled framework for predicting the next gaze target and the empirical measurement of the human cost for gaze switches independently of image content. We introduce an algorithm in the framework of sequential decision making, which converts any static saliency map into a sequence of dynamic history-dependent value maps, which are recomputed after each gaze shift. These maps are based on 1) a saliency map provided by an arbitrary saliency model, 2) the recently measured human cost function quantifying preferences in magnitude and direction of eye movements, and 3) a sequential exploration bonus, which changes with each subsequent gaze shift. The parameters of the spatial extent and temporal decay of this exploration bonus are estimated from human gaze data. The relative contributions of these three components were optimized on the MIT1003 dataset for the NSS score and are sufficient to significantly outperform predictions of the next gaze target on NSS and AUC scores for five state of the art saliency models on three image data sets.
Aggregating Bilateral Attention for Few-Shot Instance Localization

Attention filtering under various learning scenarios has proven advantageous in enhancing the performance of many neural network architectures. The mainstream attention mechanism is established upon the non-local block, also known as an essential component of the prominent Transformer networks, to catch long-range correlations. However, such unilateral attention is often hampered by sparse and obscure responses, revealing insufficient dependencies across images/patches, and high computational cost, especially for those employing the multi-head design. To overcome these issues, we introduce a novel mechanism of aggregating bilateral attention (ABA) and validate its usefulness in tackling the task of few-shot instance localization, reflecting the underlying query-support dependency. Specifically, our method facilitates uncovering informative features via assessing: i) an embedding norm for exploring the semantically-related cues; ii) context awareness for correlating the query data and support regions. ABA is then carried out by integrating the affinity relations derived from the two measurements to serve as a lightweight but effective query-support attention mechanism with high localization recall. We evaluate ABA on two localization tasks, namely, few-shot action localization and one-shot object detection. Extensive experiments demonstrate that the proposed ABA achieves superior performances over existing methods.
Multimodal Fused Deep Learning Networks for Domain Specific Image Similarity Search

The exponential increase in data over the past few years, particularly in images, has led to more complex content since visual representation became the new norm.E-commerce and similar platforms maintain large image catalogues of their products.In image databases, searching and retrieving similar images is still a challenge, even though several image retrieval techniques have been proposed over the decade.Most of these techniques work well when querying general image databases.However, they often fail in domain-specific image databases, especially for datasets with low intraclass variance.This paper proposes a domain-specific image similarity search engine based on a fused deep learning network.The network is comprised of an improved object localization module, a classification module to narrow down search options and finally a feature extraction and similarity calculation module.The network features both an offline stage for indexing the dataset and an online stage for querying.The dataset used to evaluate the performance of the proposed network is a custom domain-specific dataset related to cosmetics packaging gathered from various online platforms.The proposed method addresses the intraclass variance problem with more precise object localization and the introduction of top result reranking based on object contours.Finally, quantitative and qualitative experiment results are presented, showing improved image similarity search performance.
DRAMA: Joint Risk Localization and Captioning in Driving

Considering the functionality of situational awareness in safety-critical automation systems, the perception of risk in driving scenes and its explainability is of particular importance for autonomous and cooperative driving. Toward this goal, this paper proposes a new research direction of joint risk localization in driving scenes and its risk explanation as a natural language description. Due to the lack of standard benchmarks, we collected a large-scale dataset, DRAMA (Driving Risk Assessment Mechanism with A captioning module), which consists of 17,785 interactive driving scenarios collected in Tokyo, Japan. Our DRAMA dataset accommodates video- and object-level questions on driving risks with associated important objects to achieve the goal of visual captioning as a free-form language description utilizing closed and open-ended responses for multi-level questions, which can be used to evaluate a range of visual captioning capabilities in driving scenarios. We make this data available to the community for further re-search. Using DRAMA, we explore multiple facets of joint risk localization and captioning in interactive driving scenarios. In particular, we benchmark various multi-task pre-diction architectures and provide a detailed analysis of joint risk localization and risk captioning. The data set is available at https://usa.honda-ri.com/drama
One-Shot Synthesis of Images and Segmentation Masks

Joint synthesis of images and segmentation masks with generative adversarial networks (GANs) is promising to reduce the effort needed for collecting image data with pixel-wise annotations. However, to learn high-fidelity image-mask synthesis, existing GAN approaches first need a pre-training phase requiring large amounts of image data, which limits their utilization in restricted image domains. In this work, we take a step to reduce this limitation, introducing the task of one-shot image-mask synthesis. We aim to generate diverse images and their segmentation masks given only a single labelled example, and assuming, contrary to previous models, no access to any pre-training data. To this end, inspired by the recent architectural developments of single-image GANs, we introduce our OSMIS model which enables the synthesis of segmentation masks that are precisely aligned to the generated images in the one-shot regime. Besides achieving the high fidelity of generated masks, OSMIS outperforms state-of-the-art single-image GAN models in image synthesis quality and diversity. In addition, despite not using any additional data, OSMIS demonstrates an impressive ability to serve as a source of useful data augmentation for one-shot segmentation applications, providing performance gains that are complementary to standard data augmentation techniques. Code is available at https://github.com/boschresearch/one-shot-synthesis.
Universal Deep Image Compression via Content-Adaptive Optimization with Adapters

Deep image compression performs better than conventional codecs, such as JPEG, on natural images. However, deep image compression is learning-based and en-counters a problem: the compression performance deteriorates significantly for out-of-domain images. In this study, we highlight this problem and address a novel task: universal deep image compression. This task aims to compress images belonging to arbitrary domains, such as natural images, line drawings, and comics. To address this problem, we propose a content-adaptive optimization framework; this framework uses a pre-trained compression model and adapts the model to a target image during compression. Adapters are inserted into the decoder of the model. For each input image, our framework optimizes the latent representation extracted by the encoder and the adapter parameters in terms of rate-distortion. The adapter parameters are additionally transmitted per image. For the experiments, a benchmark dataset containing uncompressed images of four domains (natural images, line drawings, comics, and vector arts) is constructed and the proposed universal deep compression is evaluated. Finally, the proposed model is compared with non-adaptive and existing adaptive compression models. The comparison reveals that the proposed model outperforms these. The code and dataset are publicly available at https://github.com/kktsubota/universal-dic.
Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

Every application in a smart city environment like the smart grid, health monitoring, security, and surveillance generates non-stationary data streams.Due to such nature, the statistical properties of data changes over time, leading to class imbalance and concept drift issues.Both these issues cause model performance degradation.Most of the current work has been focused on developing an ensemble strategy by training a new classifier on the latest data to resolve the issue.These techniques suffer while training the new classifier if the data is imbalanced.Also, the class imbalance ratio may change greatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of class imbalance and concept drift are lacking in understating of correlation of one problem with the other.This work studies the association between concept drift and class imbalance ratio and then demonstrates how changes in class imbalance ratio along with concept drift affect the classifier's performance.We analyzed the effect of both the issues on minority and majority classes individually.To do this, we conducted experiments on benchmark datasets using state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure the performance.Our findings show that when both class imbalance and concept drift problems occur together the performance can decrease up to 15%.Our results also show that the increase in the imbalance ratio can cause a 10% to 15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutions that can cope with the challenges of non-stationary data streams like concept drift and class imbalance.
A neural video codec with spatial rate-distortion control

Neural video compression algorithms are nearly competitive with hand-crafted codecs in terms of rate-distortion performance and subjective quality. However, many neural codecs are inflexible black boxes, and give users little to no control over the reconstruction quality and bitrate. In this work, we present a flexible neural video codec that combines ideas from variable-bitrate codecs and region-of-interest-based coding. By conditioning our model on a global rate-distortion tradeoff parameter and a region-of-interest (ROI) mask, we obtain dynamic control over the per-frame bitrate and the reconstruction quality in the ROI at test time. The resulting codec enables practical use cases such as coding under bitrate constraints with fixed ROI quality, while taking a negligible hit in performance compared to a fixed-rate model. We find that our codec performs best on sequences with complex motion, where we substantially outperform non-ROI codecs in the region of interest with Bjøntegaard-Delta rate savings exceeding 60%.
Center-aware Adversarial Augmentation for Single Domain Generalization

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (Single-DG) has recently emerged to tackle a more challenging, yet realistic setting, where only one source domain is available at training time. The existing Single-DG approaches typically are based on data augmentation strategies and aim to expand the span of source data by augmenting out-of-domain samples. Generally speaking, they aim to generate hard examples to confuse the classifier. While this may make the classifier robust to small perturbation, the generated samples are typically not diverse enough to mimic a large domain shift, resulting in sub-optimal generalization performance. To alleviate this, we propose a center-aware adversarial augmentation technique that expands the source distribution by altering the source samples so as to push them away from the class centers via a novel angular center loss. We conduct extensive experiments to demonstrate the effectiveness of our approach on several benchmark datasets for Single-DG and show that our method outperforms the state-of-the-art in most cases.
Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels

Many state-of-the-art noisy-label learning methods rely on learning mechanisms that estimate the samples’ clean labels during training and discard their original noisy labels. However, this approach prevents the learning of the relationship between images, noisy labels and clean labels, which has been shown to be useful when dealing with instance-dependent label noise problems. Further-more, methods that do aim to learn this relationship re-quire cleanly annotated subsets of data, as well as distillation or multi-faceted models for training. In this paper, we propose a new training algorithm that relies on a simple model to learn the relationship between clean and noisy labels without the need for a cleanly labelled subset of data. Our algorithm follows a 3-stage process, namely: 1) self-supervised pre-training followed by an early-stopping training of the classifier to confidently predict clean labels for a subset of the training set; 2) use the clean set from stage (1) to bootstrap the relationship between images, noisy labels and clean labels, which we exploit for effective relabelling of the remaining training set using semi-supervised learning; and 3) supervised training of the classifier with all relabelled samples from stage (2). By learning this relationship, we achieve state-of-the-art performance in asymmetric and instance-dependent label noise problems <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Code is available at https://github.com/btsmart/bootstrapping-label-noise.
LexDeep: Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product.Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media.However, limited research has been conducted in this domain using the LexDeep approach.This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic.First, tweet data were collected using Twint with relevant keywords before (9 March 2019 to 17 March 2020) and during (18 March 2020 to 21 August 2021) the pandemic.Subsequently, the tweets were annotated using VADER (lexiconbased) and fed into deep learning classifiers, and experiments were conducted using several embeddings, namely simple embedding, Global Vectors, and Word2Vec, to classify the sentiments expressed in the tweets.The performance of each LexDeep model was evaluated and compared with that of a support vector machine (SVM).Finally, the unemployment rates before and during COVID-19 were analysed to gain insights into the differences in unemployment percentages through social media input and analysis.The results demonstrated that all LexDeep models with simple embedding outperformed the SVM.This confirmed the superiority of the proposed LexDeep model over a classical machine learning classifier in performing sentiment analysis tasks for domain-specific sentiments.In terms of the risk of income loss, the unemployment issue is highly politicised on both the regional and global scales; thus, if a country cannot combat this issue, the global economy will also be affected.Future research should develop a utility maximisation algorithm for household welfare evaluation, given the percentage risk of income loss owing to COVID-19.