Abstract

In this work, we present IDD-CRS, a large-scale dataset focused on critical road scenarios, captured using Advanced Driver Assistance Systems (ADAS) and dash cameras. Unlike existing datasets that predominantly emphasize pedestrian safety and vehicle safety separately, IDD-CRS incorporates both vehicle and pedestrian behaviors, offering a more comprehensive view of road safety. The dataset includes diverse scenarios, such as high-speed lane changes, unsafe vehicle approaches to pedestrians and cyclists, and complex interactions between ego vehicles and other road agents. Leveraging ADAS technology allows us to accurately define the temporal boundaries of actions, resulting in precise annotations and more reliable safety analysis. With 90 hours of video footage, consisting of 5400 one-minute-long videos and 135,000 frames, IDD-CRS introduces new vehicle related classes and hard negative classes, establishing baselines for action recognition and long-tail action recognition tasks. Our benchmarks reveal the limitations of current models, pointing toward future advancements needed for improving road safety technology.

Abstract

Despite the progress seen in classification methods, current approaches for handling videos with distribution shifts in source and target domains remain source-dependent as they require access to the source data during the adaptation stage. In this paper, we present a self-training based source-free video domain adaptation approach to address this challenge by bridging the gap between the source and the target domains. We use the source pre-trained model to generate pseudo-labels for the target domain samples, which are inevitably noisy. Thus, we treat the problem of source-free video domain adaptation as learning from noisy labels and argue that the samples with correct pseudo-labels can help us in adaptation. To this end, we leverage the cross-entropy loss as an indicator of the correctness of the pseudo-labels and use the resulting small-loss samples from the target domain for fine-tuning the model. We further enhance the adaptation performance by implementing a teacher–student (TS) framework, in which the teacher, which is updated gradually, produces reliable pseudo-labels. Meanwhile, the student undergoes fine-tuning on the target domain videos using these generated pseudo-labels to improve its performance. Extensive experimental evaluations show that our methods, termed as CleanAdapt, CleanAdapt + TS, achieve state-of-the-art results, outperforming the existing approaches on various open datasets.

Abstract

We present TabGuard, a privacy-preserving framework for an end-to-end secure Table Structure Recognition. Tab-Guard masks all the contents of the table locally and utilizes the masked table image for structure recognition. Our method is simple yet effective for detecting table cells while preserving the inherent table alignment characteristics to reconstruct tables. Our approach benefits from inductive bias, expressed through an approximated table grid which helps alleviate challenges in the detection of cells that are small or have extreme aspect ratios. Experimental results demonstrate that our solution not only establishes a new state-of-the-art on several benchmark datasets but also effectively addresses long-standing challenges associated with dense tables having complex layouts.

Abstract

Automated segmentation of medical image volumes promises to reduce costly medical experts’ time for annotation. However, using machine learning for the task is challenging due to variations in imaging modalities and scarcity of patient data. While interactive image segmentation methods and foundational models incorporating user-provided prompts to refine segmentation masks have shown promise, they overlook crucial sequential information between the slices in 3D medical image volumes and videos, resulting in discontinuities in the segmentation results. This paper proposes a new framework that dynamically updates model parameters during inference in a test time training framework using user-provided scribbles. Our framework preserves acquired knowledge from the previous slices of the current medical volume and the training dataset via student-teacher learning. We evaluate our method on diverse CT, MRI, and microscopic cell datasets. Our framework significantly reduces user annotation time by a factor of 6.72×. Compared to other interactive segmentation methods, we reduce the time by a factor of 2.64×. Our method also outperforms prompting foundation models for segmentation by achieving a dice score of 0.9 in 3-4 interactions compared to 5-8 user interactions for the foundation model, significantly reducing annotation time for the CT and MRI volumes.

Abstract

No Results Found

Abstract

The difficulty of reliably extracting characters had delayed the character recognition solutions (or OCRs) in Indian languages. Contemporary research in Indian language text recognition has shifted towards recognizing text in word or line images without requiring sub-word segmentation, leveraging Connectionist Temporal Classification (CTC) for modeling unsegmented sequences. The next challenge is the lack of public data for all these languages. And there is an immediate need to lower the entry barrier for startups or solution providers. With this in mind, (i) we introduce Mozhi dataset, a novel public dataset comprising over 1.2 million annotated word images (equivalent to approximately 120 thousand text line images) across 13 languages. (ii) We conduct a comprehensive empirical analysis of various neural network models employing CTC across 13 Indian languages. (iii) We also provide APIs for our OCR models and web-based applications that integrate these APIs to digitize Indic printed documents. We compare our model’s performance with popular publicly available OCR tools for end-to-end document image recognition. Our model outperform these OCR engines on 8 out of 13 languages.

Abstract

This paper presents a diverse compilation of Indic offline handwritten documents. Our dataset comprises 91K handwritten document images captured through unconstrained camera across thirteen Indic languages: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Manipuri, Marathi, Oriya, Punjabi, Tamil, Telugu, and Urdu, contributed by 1,220 writers. This dataset encompasses 2600K words and includes 566,187 unique words featuring diverse content types, such as alphabetic and numeric. Additionally, we establish a high baseline for the proposed dataset, facilitating evaluation, benchmarking and explicitly focusing on word recognition tasks. Our findings indicate that our dataset is an effective training source for enhancing performance on respective datasets.

Abstract

The recent surge in the vehicle market has led to an alarming increase in road accidents. This underscores the critical importance of enhancing road safety measures, particularly for vulnerable road users like motorcyclists. Hence, we introduce the rider intention prediction (RIP) competition that aims to address challenges in rider safety by proactively predicting maneuvers before they occur, thereby strengthening rider safety. This capability enables the riders to react to the potential incorrect maneuvers flagged by advanced driver assistance systems (ADAS). We collect a new dataset, namely, rider action anticipation dataset (RAAD) for the competition consisting of two tasks: single-view RIP and multi-view RIP. The dataset incorporates a spectrum of traffic conditions and challenging navigational maneuvers on roads with varying lighting conditions. For the competition, we received seventy-five registrations and five team submissions for inference of which we compared the methods of the top three performing teams on both the RIP tasks: one state-space model (Mamba2) and two learning-based approaches (SVM and CNN-LSTM). The results indicate that the state-space model outperformed the other methods across the entire dataset, providing a balanced performance across maneuver classes. The SVM-based RIP method showed the second-best performance when using random sampling and SMOTE. However, the CNN-LSTM method underperformed, primarily due to class imbalance issues, particularly struggling with minority classes. This paper details the proposed RAAD dataset and provides a summary of the submissions for the RIP 2024 competition.

Abstract

The problem of determining geolocation through visual inputs, known as Visual Place Recognition (VPR), has attracted significant attention in recent years owing to its potential applications in autonomous self-driving systems. The rising interest in these applications poses unique challenges, particularly the necessity for datasets encompassing unstructured environmental conditions to facilitate the development of robust VPR methods. In this paper, we address the VPR challenges by proposing an Indian driving VPR dataset that caters to the semantic diversity of unstructured driving environments like occlusions due to dynamic environments, variations in traffic density, viewpoint variability, and variability in lighting conditions. In unstructured driving environments, GPS signals are unreliable often affecting the vehicle to accurately determine location. To address this challenge, we develop an interactive image-to-image tagging annotation tool to annotate large datasets with ground truth annotations for VPR training. Evaluation of the state-of-the-art methods on our dataset shows a significant performance drop of up to 15%, defeating a large number of standard VPR datasets. We also provide an exhaustive quantitative and qualitative experimental analysis of frontal-view, multi-view, and sequence-matching methods. We believe that our dataset will open new challenges for the VPR research community to build robust models.

Abstract

With the increased importance of autonomous navigation systems has come an increasing need to protect the safety of Vulnerable Road Users (VRUs) such as pedestrians. Predicting pedestrian intent is one such challenging task, where prior work predicts the binary cross/no-cross intention with a fusion of visual and motion features. However, there has been no effort so far to hedge such predictions with humanunderstandable reasons. We address this issue by introducing a novel problem setting of exploring the intuitive reasoning behind a pedestrian’s intent. In particular, we show that predicting the ‘WHY’ can be very useful in understanding the ‘WHAT’. To this end, we propose a novel, reasonenriched PIE++ dataset consisting of multi-label textual explanations/reasons for pedestrian intent. We also introduce a novel multi-task learning framework called MINDREAD, which leverages a cross-modal representation learning framework for predicting pedestrian intent as well as the reason behind the intent. Our comprehensive experiments show significant improvement of 5.6% and 7% in accuracy and F1-score for the task of intent prediction on the PIE++ dataset using MINDREAD. We also achieved a 4.4% improvement in accuracy on a commonly used JAAD dataset. Extensive evaluation using quantitative/qualitative metrics and user studies shows the effectiveness of our approach.