Abstract

Histopathology image analysis is widely accepted as a gold standard for cancer diagnosis. The Cancer Genome Atlas (TCGA) contains large repositories of histopathology whole slide images spanning several organs and subtypes. However, not much work has gone into analyzing all the organs and subtypes and their similarities. Our work attempts to bridge this gap by training deep learning models to classify cancer vs. normal patches for 11 subtypes spanning seven organs (9,792 tissue slides) to achieve high classification performance. We used these models to investigate their performances in the test set of other organs (cross-organ inference). We found that every model had a good cross-organ inference accuracy when tested on breast, colorectal, and liver cancers. Further, high accuracy is observed between models trained on the cancer subtypes originating from the same organ (kidney and lung). We also validated these performances by showing the separability of cancer and normal samples in a high-dimensional feature space. We further hypothesized that the high cross-organ inferences are due to shared tumor morphologies among organs. We validated the hypothesis by showing the overlap in the Gradient-weighted Class Activation Mapping (GradCAM) visualizations and similarities in the distributions of nuclei features present within the high-attention regions.

Abstract

Reading Indian scene texts is complex due to the use of regional vocabulary, multiple fonts/scripts, and text size. This work investigates the significant differences in Indian and Latin Scene Text Recognition (STR) systems. Recent STR works rely on synthetic generators that involve diverse fonts to ensure robust reading solutions. We present utilizing additional non-Unicode fonts with generally employed Unicode fonts to cover font diversity in such synthesizers for Indian languages. We also perform experiments on transfer learning among six different Indian languages. Our transfer learning experiments on synthetic images with common backgrounds provide an exciting insight that Indian scripts can benefit from each other than from the extensive English datasets. Our evaluations for the real settings help us achieve significant improvements over previous methods on four Indian languages from standard datasets like IIIT-ILST, MLT-17, and the new dataset (we release) containing 440 scene images with 500 Gujarati and 2535 Tamil words. Further enriching the synthetic dataset with non-Unicode fonts and multiple augmentations helps us achieve a remarkable Word Recognition Rate gain of over 33% on the IIIT-ILST Hindi dataset. We also present the results of lexicon-based transcription approaches for all six languages

Abstract

In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are a significant source of fatalities involving motorcycles. Identifying and penalizing such riders is vital in curbing road accidents and improving citizens’ safety. With this motivation, we propose an approach for detecting, tracking, and counting motorcycle riding violations in videos taken from a vehicle-mounted dashboard camera. We employ a curriculum learning-based object detector to better tackle challenging scenarios such as occlusions. We introduce a novel trapezium-shaped object boundary representation to increase robustness and tackle the rider-motorcycle association. We also introduce an amodal regressor that generates bounding boxes for the occluded riders. Experimental results on a large-scale unconstrained driving dataset demonstrate the superiority of our approach compared to existing approaches and other ablative variants.

Abstract

Self-supervised methods have shown promising results in denoising and dehazing tasks, where the collection of the paired dataset is challenging and expensive. However, we find that these methods fail to remove the rain streaks when applied for image deraining tasks. The method’s poor per- formance is due to the explicit assumptions: (i) the distribu- tion of noise or haze is uniform and (ii) the value of a noisy or hazy pixel is independent of its neighbors. The rainy pix- els are non-uniformly distributed, and it is not necessarily dependant on its neighboring pixels. Hence, we conclude that the self-supervised method needs to have some prior knowledge about rain distribution to perform the deraining task. To provide this knowledge, we hypothesize a network trained with minimal supervision to estimate the likelihood of rainy pixels. This leads us to our proposed method called FLUID: Few Shot Self-Supervised Image Deraining. We perform extensive experiments and comparisons with existing image deraining and few-shot image-to-image translation methods on Rain 100L and DDN-SIRR datasets containing real and synthetic rainy images. In addition, we use the Rainy Cityscapes dataset to show that our method trained in a few-shot setting can improve semantic segmen- tation and object detection in rainy conditions. Our ap- proach obtains a mIoU gain of 51.20 over the current best- performing deraining method.

Abstract

Restoring images degraded due to atmospheric turbulence is challenging as it consists of several distortions. Several deep learning methods have been proposed to minimize atmospheric distortions that consist of a single-stage deep net- work. However, we find that a single-stage deep network is insuffi- cient to remove the mixture of distortions caused by atmospheric turbulence. We propose a two-stage deep adversarial network that minimizes atmospheric turbulence to mitigate this. The first stage reduces the geometrical distortion and the second stage minimizes the image blur. We improve our network by adding channel attention and a proposed sub-pixel mechanism, which utilizes the information between the channels and further reduces the atmospheric turbulence at the finer level. Unlike previous methods, our approach neither uses any prior knowledge about atmospheric turbulence conditions at inference time nor requires the fusion of multiple images to get a single restored image. Our final restoration models DT-GAN+ and DTD-GAN+ outperform the general state-of-the-art image-to-image translation models and baseline restoration models. We synthesize turbulent image datasets to train the restoration models. Additionally, we also curate a natural turbulent dataset from YouTube to show the generalisability of the proposed model. We perform extensive experiments on restored images by utilizing them for downstream tasks such as classification, pose estimation, semantic keypoint estimation, and depth estimation. We observe that our restored images outperform turbulent images in downstream tasks by a significant margin demonstrating the restoration model’s applica- bility in real-world problems.

Abstract

Infographics communicate information using a combi- nation of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly rea- son over the document layout, textual content, graphical el- ements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reason- ing and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results sug- gest that VQA on infographics—images that are designed to communicate information quickly and clearly to human brain—is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa.org

Abstract

Recent efforts in multi-domain learning for semantic seg- mentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experi- ment performed sequentially on three popular road scene segmentation datasets demonstrates that existing segmenta- tion frameworks fail at incrementally learning on a series of visually disparate geographical domains. When learning a new domain, the model catastrophically forgets previously learned knowledge. In this work, we pose the problem of multi-domain incremental learning for semantic segmenta- tion. Given a model trained on a particular geographical domain, the goal is to (i) incrementally learn a new geo- graphical domain, (ii) while retaining performance on the old domain, (iii) given that the previous domain’s dataset is not accessible. We propose a dynamic architecture that assigns universally shared, domain-invariant parameters to capture homogeneous semantic features present in all do- mains, while dedicated domain-specific parameters learn the statistics of each domain. Our novel optimization strat- egy helps achieve a good balance between retention of old knowledge (stability) and acquiring new knowledge (plas- ticity). We demonstrate the effectiveness of our proposed solution on domain incremental settings pertaining to real- world driving scenes from roads of Germany (Cityscapes), the United States (BDD100k), and India (IDD).

Abstract

Table structure recognition is necessary for a compre- hensive understanding of documents. Tables in unstruc- tured business documents are tough to parse due to the high diversity of layouts, varying alignments of contents, and the presence of empty cells. The problem is particularly dif- ficult because of challenges in identifying individual cells using visual or linguistic contexts or both. Accurate detec- tion of table cells (including empty cells) simplifies struc- ture extraction and hence, it becomes the prime focus of our work. We propose a novel object-detection-based deep model that captures the inherent alignments of cells within tables and is fine-tuned for fast optimization. Despite ac- curate detection of cells, recognizing structures for dense tables may still be challenging because of difficulties in cap- turing long-range row/column dependencies in presence of multi-row/column spanning cells. Therefore, we also aim to improve structure recognition by deducing a novel rec- tilinear graph-based formulation. From a semantics per- spective, we highlight the significance of empty cells in a table. To take these cells into account, we suggest an en- hancement to a popular evaluation criterion. Finally, we introduce a modestly sized evaluation dataset with an anno- tation style inspired by human cognition to encourage new approaches to the problem. Our framework improves the previous state-of-the-art performance by a 2.7% average F1-score on benchmark datasets.

Abstract

Advancements in adaptive object detection can lead to tremendous improvements in applications like autonomous navigation, as they alleviate the distributional shifts along the detection pipeline. Prior works adopt adversarial learn- ing to align image features at global and local levels, yet the instance-specific misalignment persists. Also, adaptive object detection remains challenging due to visual diver- sity in background scenes and intricate combinations of ob- jects. Motivated by structural importance, we aim to attend prominent instance-specific regions, overcoming the feature misalignment issue. We propose a novel resIduaL seLf- attentive featUre alignMEnt (ILLUME) method for adaptive object detection. ILLUME comprises Self-Attention Fea- ture Map (SAFM) module that enhances structural atten- tion to object-related regions and thereby generates domain invariant features. Our approach significantly reduces the domain distance with the improved feature alignment of the instances. Qualitative results demonstrate the ability of ILLUME to attend important object instances required for alignment. Experimental results on several benchmark datasets show that our method outperforms the existing state-of-the-art approaches.

Abstract

Player detection lays the foundation for many applica- tions in the field of sports analytics including player recog- nition, player tracking, and activity detection. In this work, we study player detection in continuous long shot broad- cast videos. Broadcast match videos are easy to obtain, and detection on these videos is much more challenging. We propose a transductive approach for player detection that treats it as a domain adaptation problem. We show that instance-level domain labels are significant for sufficient adaptation in the case of soccer broadcast videos. An ef- ficient multi-model greedy labelling scheme based on visual features is proposed to annotate domain labels on bounding box predictions made by our inductive model. We use reli- able instances from the inductive model inferences to train a transductive copy of the model. We create and release a fully annotated player detection dataset comprising soccer broadcast videos from the FIFA 2018 World Cup matches to evaluate our method. Our method shows significant im- provements in player detection to the baseline and existing state-of-the-art methods on our dataset. We show, on aver- age, a 16 point improvement in mAP for soccer broadcast videos by annotating domain labels for around a 100 sam- ples per video.