Abstract

Due to the lack of a large annotated corpus, many resource-poor Indian languages struggle to reap the beneits of recent deep feature representations in Natural Language Processing (NLP). Moreover, adopting existing language models trained on large English corpora for Indian languages is oten limited by data availability, rich morphological variation, syntax, and semantic diferences. In this paper, we explore the traditional to recent eicient representations to overcome the challenges of low resource language, Telugu. In particular, our main objective is to mitigate the low-resource problem for Telugu. Overall, we present several contributions to a resource-poor language viz. Telugu. (i) a large annotated data (35,142 sentences in each task) for multiple NLP tasks such as sentiment analysis, emotion identiication, hate-speech detection, and sarcasm detection, (ii) we create diferent lexicons for sentiment, emotion, and hate-speech for improving the eiciency of the models, (iii) pretrained word and sentence embeddings, and (iv) diferent pretrained language models for Telugu such as ELMo-Te, BERT-Te, RoBERTa-Te, ALBERT-Te, and DistilBERT-Te on a large Telugu corpus consisting of 80,15,588 sentences (16,37,408 sentences from Telugu Wikipedia and 63,78,180 sentences crawled from diferent Telugu websites). Further, we show that these representations signiicantly improve the performance of four NLP tasks and present the benchmark results for Telugu. We argue that our pretrained embeddings are competitive or beter than the existing multilingual pretrained models: mBERT, XLM-R, and IndicBERT. Lastly, the ine-tuning of pretrained models show higher performance than linear probing results on four NLP tasks with following F1-scores: Sentiment (68.72), Emotion (58.04), Hate-Speech (64.27) and Sarcasm (77.93). We also experiment on publicly available Telugu datasets (Named Entity Recognition, Article Genre Classiication, and Sentiment Analysis ), ind that our Telugu pretrained language models (BERT-Te and RoBERTa-Te) outperform the state-of-the-art system except for the sentiment task. We open-source our corpus, four diferent datasets, lexicons, embeddings, and code https://github.com/Cha14ran/DREAM-T. he pretrained Transformer models for Telugu are available at https://huggingface.co/ltrctelugu.

Abstract

In this work, we propose IndoLayout, a novel real-time approach for generating high-quality occupancy maps from an RGB image for indoor scenes. Such occupancy maps are often crucial for path-planning and mapping in indoor environments but are often built using only information contained in the ego view. In contrast, our approach also predicts occupancy values beyond immediately visible regions from just a monocular image, leveraging learnt priors from indoor scenes. Hence, our proposed network can produce a hallucinated, amodal scene layout that includes areas occluded in the RGB image, such as a navigable floor behind a desk. Specifically, we propose a novel architecture that uses self-attention and adversarial learning to vastly improve the quality of the predicted layout. We evaluate our model on several photorealistic indoor datasets and outperform previous relevant work on all metrics that measure layout quality, including newly adopted ones. Finally, we demonstrate the effectiveness of our method by showing significant improvements on the PointNav task over similar approaches using IndoLayout. For more details, please refer to the project page: https://indolayout.github.io/.

Abstract

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/

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Abstract

Despite existing works of Audio-Visual Saliency Prediction (AVSP) models claiming to achieve promising results by fusing audio modality over visual-only models, these models fail to leverage audio information. In this paper, we investigate the relevance of audio cues in conjunction with the visual ones and conduct extensive analysis by employing well-established audio modules and fusion techniques from diverse correlated audio-visual tasks. Our analysis on ten diverse saliency datasets suggests that none of the methods worked for incorporating audio. Furthermore, we bring to light, why AVSP models show a gain in performance over visual-only models, though the audio branch is agnostic at inference. Our work questions the role of audio in current deep AVSP models and motivates the community to a clear avenue for reconsideration of the complex architectures by demonstrating that simpler alternatives work equally well.

Abstract

We investigate the task of complex NER for the English language. The task is non-trivial due to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities in the prevalent literature. Using pre-trained language models such as BERT, we obtain a competitive performance on this task. We qualitatively analyze the performance of multiple architectures for this task. All our models are able to outperform the baseline by a significant margin. Our best performing model beats the baseline F1-score by over 9%.

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We introduce the task of detection of competing model entities from scientific documents. We define competing models as those models that solve a particular task that is investigated in the target research document. The task is challenging due to the fact that contextual information is required from the entire target document to predict the model entities. Hence, traditional sequence labelling approaches fail in such settings. Furthermore, model entities themselves are long-tailed in nature, i.e, their prevalence in scientific literature is limited, along with a scarcity of labelled data for training supervised learning techniques. To address the above bottlenecks, we combine an Unsupervised Graph Ranking algorithm with a SciBERT-CRF based sequence labeller to predict the entities. We introduce a strong baseline using the above mentioned pipeline. Also, to address the label scarcity of long-tailed model entities, we use distant supervision leveraging an external Knowledge Base (KB) to generate synthetic training data. We address the problem of overfitting in small sized datasets for supervised NER baselines using a simple entity replacement technique. We introduce this model as part of a starting point for an end-to-end automated framework to extract relevant model names and link them with their respective cited papers from research documents. We believe this task will serve as an important starting point to map the research landscape of computer science in a scalable manner, needing minimal human intervention. The code and dataset is available in the given link : https://github.com/Swayatta/Competing-Models

Abstract

Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score

Abstract

An autonomous broker that liaises between retail customers and power-generating companies (GenCos) is essential for the smart grid ecosystem. The effciency brought in by such brokers to the smart grid setup can be studied through a well-developed simulation environment. In this paper, we describe the design of one such intelligent energy broker called VidyutVanika21 (VV21) and analyze its performance using a simulation platform called PowerTAC (Power Trading Agent Competition). Specifcally, we discuss the retail (VV21–RM) and wholesale market (VV21–WM) modules of VV21 that help the broker achieve high net profts in a competitive setup. Supported by game-theoretic analysis, the VV21–RM designs tariff contracts that a) maintain a balanced portfolio of different types of customers; b) sustain an appropriate level of market share, and c) introduce surcharges on customers to reduce energy usage during peak demand times. The VV21–WM aims to reduce the cost of procurement by following the supply curve of the GenCo to identify its lowest ask for each auction which is then used to generate suitable bids. We further demonstrate the effcacy of the retail and wholesale strategies of VV21 in PowerTAC 2021 fnals and through several controlled experiments.