Abstract

Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object detection has been studied for locating missing curbs and estimating reasonable regions for pedestrians on road scene images. Such methods involve analyzing task-specific single object cues. In this paper, we present the first and most challenging video dataset for missing objects, with multiple types of traffic signs for which the cues are visible without the signs in the scenes. We refer to it as the Missing Traffic Signs Video Dataset (MTSVD). MTSVD is challenging compared to the previous works in two aspects i) The traffic signs are generally not present in the vicinity of their cues, ii) The traffic signs’ cues are diverse and unique. Also, MTSVD is the first publicly available missing object dataset. To train the models for identifying missing signs, we complement our dataset with 10K traffic sign tracks, with 40% of the traffic signs having cues visible in the scenes. For identifying missing signs, we propose the Cue-driven Contextual Attention units (CueCAn), which we incorporate in our model’s encoder. We first train the encoder to classify the presence of traffic sign cues and then train the entire segmentation model end-to-end to localize missing traffic signs. Quantitative and qualitative analysis shows that CueCAn significantly improves the performance of base models.

Abstract

Large-scale deployment of fully autonomous vehicles re- quires a very high degree of robustness to unstructured traf- fic, weather conditions, and should prevent unsafe mispre- dictions. While there are several datasets and benchmarks focusing on segmentation for drive scenes, they are not specifically focused on safety and robustness issues. We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, cap- tured under rain, fog, low light, and snow in unstructured driving conditions. As compared to other adverse weather datasets, we provide i.) more annotated images, ii.) paired Near-Infrared (NIR) image for each frame, iii.) larger label set with a 4-level label hierarchy to capture unstructured traffic conditions. We benchmark state-of-the-art models for semantic segmentation in IDD-AW. We also propose a new metric called “Safe mean Intersection over Union (Safe mIoU)” for hierarchical datasets which penalizes danger- ous mispredictions that are not captured in the traditional definition of mean Intersection over Union (mIoU). The re- sults show that IDD-AW is one of the most challenging datasets to date for these tasks. The dataset and code will be available here: http://iddaw.github.io

Abstract

Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4-ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training.

Abstract

Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias. Machine Unlearning can address these by allowing post-hoc deletion of affected training data from a learned model. Achieving this task exactly is computationally expensive; consequently, recent works have proposed inexact unlearning algorithms to solve this approximately as well as evaluation methods to test the effectiveness of these algorithms. In this work, we first outline some necessary criteria for evaluation methods and show no existing evaluation satisfies them all. Then, we design a stronger black-box evaluation method called the Interclass Confusion (IC) test which adversarially manipulates data during training to detect the insufficiency of unlearning procedures. We also propose two analytically motivated baseline methods~(EU-k and CF-k) which outperform several popular inexact unlearning methods. Overall, we demonstrate how adversarial evaluation strategies can help in analyzing various unlearning phenomena which can guide the development of stronger unlearning algorithms.

Abstract

Second language learners often make grammatical mistakes, which can impact the performance of Spoken Language Understanding (SLU) systems. SLU systems consist of two key components: Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). This work focuses on the effects of grammatical errors which were synthetically generated by manipulating Parts of Speech (POS) tokens on the NLU module. Our analysis comprises three main aspects. Firstly, we assess the impact of grammatical errors on the overall performance of NLU systems, specifically in the domains of Intent Detection and Slot Filling. Secondly, we investigate NLU performance concerning POS tags. Lastly, we utilize an Attention-based NLU model to evaluate the significance of different POS. This study evaluates NLU models on ATIS and SNIPS datasets for Intent Detection and Slot Filling tasks with introduced grammatical errors, leading to a significant performance drop as large as 4.7% for Intent Detection and 5.1% for Slot Filling. However, training on datasets with synthetic errors helps mitigate this drop, and the models are shown to be sensitive to increasing levels of corrupted tokens. Additionally, attention analysis reveals Proper Nouns’ higher weights in determining intent, while specific POS corruption, particularly Auxiliary Verbs and Nouns, is more likely to cause intent misclassification.

Abstract

The Industrial Internet of Things (IIoT) is able to connect machines, analytics and people with IoT smart devices, gateway nodes and edge devices to create powerful intuitivenesses to drive smarter, faster and effective business agreements. IIoT having interconnected machines along with devices can monitor, gather, exchange, and analyze information. Since the communication among the entities in IIoT environment takes place insecurely (for instance, wireless communications and Internet), an intruder can easily tamper with the data. Moreover, physical theft of IoT smart devices provides an intruder to mount impersonation and other attacks. To handle such critical issues, in this work, we design a new private blockchain-envisioned access control scheme for Pervasive Edge Computing (PEC) in IIoT environment, called PBACS-PECIIoT. We consider the private blockchain consisting of the transactions and registration credentials of the entities related to IIoT, because the information is strictly confidential and private. The security of PBACS-PECIIoT is significantly improved due to usage of blockchain as immutability, transparency and decentralization along with protection of various potential attacks. A meticulous comparative analysis exhibits that PBACS-PECIIoT achieves greater security and more functionality features, and requires low costs for communication and computational as compared to other pertinent schemes.

Abstract

This paper presents a trim-free, low-power, and high-precision voltage/current reference for IoT and biomedical applications. The voltage reference is generated by the proper addition of CTAT and PTAT voltages, whereas, the current reference is generated using a MOSFET in the active region, which acts like a resistor and helps in lowering power consumption without increasing the form factor. Implemented in the 90nm CMOS process, the proposed voltage/current reference outputs a voltage and current of 820mV and 3.23nA, respectively. Without trimming, the process variation of the proposed voltage/current reference is 1.34%(σ/μ) / 1.75%(σ/μ) and the temperature coefficient of the voltage/current reference is 62ppm/oC / 332ppm/oC over the temperature range of -40oC to 100oC. The line sensitivity of the voltage/current reference is 0.296%/V / 0.414%/V for a wide supply range of 1V - 3.5V. The area occupied by the total circuit is 0.0112mm2, while the total power consumption of the design is 37nW at the typical corner of 27oC and 1V supply.

Abstract

Abstract The proliferation of speech technology applications has led to an unprecedented demand for effective speech enhancement techniques, particularly in low Signal-to-Noise Ratio (SNR) conditions. This research presents a novel approach to speech enhancement, specifically designed for very low SNR scenarios. Our technique focuses on speaker embedding at a granular level and highlights its consistent impact on enhancing speech quality and improving Automatic Speech Recognition (ASR) performance, a significant downstream task. Experimental findings demonstrate competitive speech quality and substantial enhancements in ASR accuracy compared to alternative methods in low SNR situations. The proposed technique offers promising advancements in addressing the challenges posed by low SNR conditions in speech technology applications.

Abstract

Visual Place Recognition (VPR) is vital for robot localization. To date, the most performant VPR approaches are environment- and task-specific : while they exhibit strong performance in structured environments (predominantly urban driving), their performance degrades severely in unstructured environments, rendering most approaches brittle to robust real-world deployment. In this work, we develop a universal solution to VPR – a technique that works across a broad range of structured and unstructured environments (urban, outdoors, indoors, aerial, underwater, and subterranean environments) without any re-training or finetuning. We demonstrate that general-purpose feature representations derived from off-the-shelf self-supervised models with no VPR-specific training are the right substrate upon which to build such a universal VPR solution. Combining these derived features with unsupervised feature aggregation enables our suite of methods, AnyLoc , to achieve up to 4× significantly higher performance than existing approaches. We further obtain a 6% improvement in performance by characterizing the semantic properties of these features, uncovering unique domains which encapsulate datasets from similar environments. Our detailed experiments and analysis lay a foundation for building VPR solutions that may be deployed anywhere , anytime , and across anyview . We encourage the readers to explore our project page and interactive demos: https://anyloc.github.io/ .

Abstract

Deployment of Face Recognition systems on the edge has seen significant growth due to advancements in hardware design and efficient neural architectures. However, tailoring SOTA Face Recognition solutions to a specific edge device is still not easy and is vastly unexplored. Although, benchmark data is available for some combinations of model, device, and framework, it is neither comprehensive nor scalable. We propose an approximation to determine the relationship between a model and its inference time in an edge deployment scenario. Using a small number of data points, we are able to predict the throughput of custom models in an explainable manner. The prediction errors are small enough to be considered noise in observations. We also analyze which approaches are most efficient and make better use of hardware in terms of accuracy and error rates to gain a better understanding of their behaviour. Related & necessary modules such as Face Anti-Spoofing are also analyzed. To the best of our knowledge, we are the first to tackle this issue directly. The data and code along with future updates to the models and hardware will be made available at https://github.com/AyanBiswas19/Resource_Efficient_FR.