Computer Vision on Road Scenes: Benchmarking, and Open World Object Detection

Deepak Kumar Singh

Abstract

In autonomous driving, we have multiple computer vision tasks like object detection, semantic segmentation, and instance segmentation which plays a crucial role in perceiving the environment around the vehicle. Understanding the behaviour and performance of such tasks helps improve and address the key issues that are inherent in the system. There can be issues which are latent in the deep learning architecture and also in the datasets on which the deep learning models are trained and tested. In this thesis, we benchmark the performance of various popular deep learning models on road scene datasets for various computer vision tasks and also formulate open-world object detection on road scenes by addressing the inherent issues present in road scene datasets.

In the first part of the work, we aim to understand the performance and behaviour of various deep learning models on road scene datasets; Cityscapes, IDD, and BDD. Object detection, semantic segmentation, and instance segmentation form the bases for many computer vision tasks in autonomous driving. The complexity of these tasks increases as we shift from object detection to instance segmentation. The state-of-the-art models are evaluated on standard datasets such as PASCAL-VOC and MS-COCO, which does not consider the dynamics of road scenes. Driving datasets such as Cityscapes and Berkeley Deep Drive(BDD) are captured in a structured environment with better road markings and fewer variations in the appearance of objects and background. However, the same does not hold for Indian roads. The Indian Driving Dataset(IDD) dataset is captured in unstructured driving scenarios and is highly challenging for a model due to its diversity. This work presents a comprehensive evaluation of state-of-the-art models on object detection, semantic segmentation, and instance segmentation on road scene datasets. We present our analyses and compare their quantitative and qualitative performance on structured driving datasets(Cityscapes and BDD) and the unstructured driving dataset(IDD); understanding the behavior on these datasets helps in addressing various practical issues and helps in creating real-life applications.

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Role of Scene Text Understanding in Enhancing Driver Assistance

George Tom

Abstract

Scene text conveys important information to drivers and pedestrians, serving as an indispensable means of communication in various environments. Scene text contains information regarding the speed limit, route information, rest stops, and exits, among other important information. It is important for drivers and passengers to understand this information for a safe and efficient journey. However, outdoor scenes are cluttered with text, distracting drivers and making it hard to focus on what matters, potentially compromising their ability to focus on essential details and navigate safely. Recognising scene text in motion aggravates this challenge, as textual cues transiently appear and necessitate early detection at a distance. Driving scenarios introduce additional complexities, including occlusions, motion blur, perspective distortions, and varying text sizes, further complicating scene text understanding.

      In this thesis, we look at improving scene text understanding in diving scenarios through video question answering and analyzing the present state of scene text detection, recognition, and tracking: (i) We introduce new video questions answering tasks and datasets that require an understanding of text and road signs in driving videos to answer the questions. (ii) We look at the current state of scene text detection, tracking, and recognition in the driving domain through the RoadText-1K competition. (iii) We explore detection and recognition in special cases of occlusions, a common yet under-explored complication in real-world driving scenarios. By focusing on these areas, the thesis contributes to advancing scene text analysis methodologies, offering insights and solutions that are imperative for developing more intelligent and responsive driver assistance systems.

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Efficient Multimodal Video Representation Learning Through Language

Darshan Singh S

Abstract

This work presents several contributions to video representation learning and related multimodal tasks, addressing key challenges in datasets, efficient model adaptation using less data, and compositional and fine-grained visual understanding. Despite the rapid growth of online lecture videos in the past several years, video-language research has primarily focused on instructional videos/movies, resulting in a scarcity of specialized datasets for educational lecture videos. To address this, we first introduce AV Lectures, a large-scale dataset of STEM lecture videos. It consists of 86 courses with over 2,350 lectures covering various STEM subjects. Each course contains video lectures, transcripts, OCR for lecture frames, and optionally lecture notes, slides, assignments, and related educational content that can inspire a variety of tasks. Next, we propose a novel unsupervised temporal segmentation task to segment lecture videos into bite-sized topics. We show that multimodal cues can be effectively utilized to learn lecture-aware representations for this task, facilitating a richer analysis of educational content. Next, we address the inefficiency of adapting pre-trained models like CLIP to videos. Existing methods typically rely on large-scale, sparsely annotated video caption datasets, resulting in slow and data-intensive adaptation. We propose SRL-CLIP, a novel approach that leverages the rich, structured semantic information within Semantic Role Labels (SRLs) for highly efficient adaptation. We use VidSitu for adaptation as it provides dense SRL annotations that holistically represent the entire video. SRL-CLIP achieves comparable or superior performance on various video understanding benchmarks (zero-shot retrieval, situation recognition, dense video captioning, and localization) compared to state-of-the-art models that possess 4−8× more parameters and are post-pretrained on up to 4000× times more data. To further explore the models’ understanding of visual content, we introduce three novel benchmarks. First, VELOCITI evaluates the compositional reasoning abilities of video-language models, focusing on their ability to bind semantic concepts through time. Second, we introduce NDLB, a framework aimed at improving fine-grained image captioning, which uses self-retrieval as a key component along with a new benchmark to check if the model can capture subtle visual distinctions. Finally, we introduce D3, a benchmark specifically designed to evaluate the fine-grained visual discrimination capabilities of MLLMs using self-retrieval, further pushing the boundaries of fine-grained visual understanding. These contributions, which include novel datasets, efficient training recipes, and insightful benchmarks, collectively advance the state of the art in multimodal and video representation learning.

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Weakly Supervised and Deep Learning Methods for Histopathological Image Classification in Neurological and Renal Disorders

R Anirudh Reddy

Abstract

The analysis of digital histopathology slides or Whole Slide Images (WSIs) is critical for several diagnoses. Recent advancements in computational techniques, particularly in the field of digital pathology, have shown promise in automating the classification process. Whole Slide Imaging (WSI), combined with deep learning and modern computer vision techniques, has emerged as a powerful tool in this domain. This thesis addresses two major medical challenges using deep learning and computer vision techniques: the classification of Lupus Nephritis (LN) and low-grade gliomas into their respective subtypes. Systemic lupus erythematosus (SLE) is an autoimmune disease wherein the patient’s immune system attacks healthy tissues, leading to Lupus Nephritis (LN), a severe condition causing renal failure. Traditional methods for diagnosing LN require meticulous pathological assessment of renal biopsies, which is time-consuming. In the first architecture (chapter 3), We propose a novel pipeline that automates this process by: 1) detecting various glomerular patterns in WSIs using Periodic Acid-Schiff (PAS) stained images, and 2) classifying each image based on these extracted glomerular features. This approach leverages deep learning to improve the accuracy and efficiency of LN classification. Low-grade glioma, a type of brain tumor originating from glial cells, also presents significant diagnostic challenges due to the large size and complexity of WSIs. In the second architecture(chapter 4), our work involves the classification of low-grade gliomas into Astrocytoma and Oligodendroglioma. Given the computational infeasibility of training deep learning models on gigapixel images, we adopt a weakly supervised method to extract discriminative patches from WSIs, which represent the tumor regions. A Convolutional Neural Network (CNN) is then trained on these discriminative patches, and the results are aggregated to determine the WSI label. Evaluated on a dataset of 581,616 patches from 286 WSIs obtained from The Cancer Genome Atlas (TCGA) portal, our method achieved a slide-wise accuracy of 79.31%, which increased to 89.65% when trained only on discriminative patches.The methodologies presented in this thesis not only demonstrate significant improvements in classification accuracy but also offer scalable and efficient solutions for enhancing the diagnostic processes in pathology, ultimately contributing to better patient outcomes and more efficient healthcare deliver.

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