Abstract

Handwritten text recognition (htr) for Indian languages is not yet a well-studied problem. This is primarily due to the unavailability of large annotated datasets in the associated scripts. Existing datasets are small in size. They also use small lexicons. Such datasets are not sufficient to build robust solutions to htr using modern machine learning techniques. In this work, we introduce a large-scale handwritten dataset for Indic scripts containing 868K handwritten instances written by 135 writers in 8 widely-used scripts. A comprehensive dataset of ten Indic scripts are derived by combining the newly introduced dataset with the earlier datasets developed for Devanagari (iiit-hw-dev) and Telugu (iiit-hw-telugu), referred to as the iiit-indic-hw-words. We further establish a high baseline for text recognition in eight Indic scripts. Our recognition scheme follows the contemporary design principles from other recognition literature, and yields competitive results on English. iiit-indic-hw-words along with the recognizers are available publicly1. We further (i) study the reasons for changes in htr performance across scripts (ii) explore the utility of pre-training for Indic htrs. We hope our efforts will catalyze research and fuel applications related to handwritten document understanding in Indic scripts.

Abstract

We aim to solve the highly challenging task of generating continuous sign language videos solely from speech segments for the first time. Recent efforts in this space have focused on generating such videos from human-annotated text transcripts without considering other modalities. However, replacing speech with sign language proves to be a practical solution while communicating with people suffering from hearing loss. Therefore, we eliminate the need of using text as input and design techniques that work for more natural, continuous, freely uttered speech covering an extensive vocabulary. Since the current datasets are inadequate for generating sign language directly from speech, we collect and release the first Indian sign language dataset comprising speech-level annotations, text transcripts, and the corresponding sign-language videos. Next, we propose a multi-tasking transformer network trained to generate signer’s poses from speech segments. With speech-to-text as an auxiliary task and an additional cross-modal discriminator, our model learns to generate continuous sign pose sequences in an end-to-end manner. Extensive experiments and comparisons with other baselines demonstrate the effectiveness of our approach. We also conduct additional ablation studies to analyze the effect of different modules of our network. A demo video containing several results is attached to the supplementary material. Index T

Abstract

This work studies the long-standing problems of model capacity and negative interference in multilingual neural machine translation (MNMT). We use network pruning techniques and observe that pruning 50-70% of the parameters from a trained MNMT model results only in a 0.29-1.98 drop in the BLEU score. Suggesting that there exist large redundancies even in MNMT models. These observations motivate us to use the redundant parameters and counter the interference problem efficiently. We propose a novel adaptation strategy, where we iteratively prune and retrain the redundant parameters of an MNMT to improve bilingual representations while retaining the multilinguality. Negative interference severely affects high resource languages, and our method alleviates it without any additional adapter modules. Hence, we call it parameterfree adaptation strategy, paving way for the efficient adaptation of MNMT. We demonstrate the effectiveness of our method on a 9 language MNMT trained on TED talks, and report an average improvement of +1.36 BLEU on high resource pairs. Code will be released

Abstract

rametric models that represent layout in terms of scene attributes are an attractive avenue for road scene understanding in autonomous navigation. Prior works that rely only on ground imagery are limited by the narrow field of view of the camera, occlusions and perspective foreshortening. In this paper, we demonstrate the effectiveness of using aerial imagery as an additional modality to overcome the above challenges. We propose a novel architecture, Unified, that combines features from both aerial and ground imagery to infer scene attributes. We quantitatively evaluate on the KITTI dataset and show that our Unified model outperforms prior works. Since this dataset is limited to road scenes close to the vehicle, we supplement the publicly available Argoverse dataset with scene attribute annotations and evaluate on far-away scenes. We show both quantitatively and qualitatively, the importance of aerial imagery in understanding road scenes, especially in regions farther away from the ego-vehicle. All code, models, and data, including scene attribute annotations on the Argoverse dataset along with collected and processed aerial imagery, are available

Abstract

Indian language machine translation performance is hampered due to the lack of large scale multi-lingual sentence aligned corpora and robust benchmarks. Through this paper, we provide and analyse an automated framework to obtain such a corpus for Indian language neural machine translation (NMT) systems. Our pipeline consists of a baseline NMT system, a retrieval module, and an alignment module that is used to work with publicly available websites such as press releases by the government. The main contribution towards this effort is to obtain an incremental method that uses the above pipeline to iteratively improve the size of the corpus as well as improve each of the components of our system. Through our work, we also evaluate the design choices such as the choice of pivoting language and the effect of iterative incremental increase in corpus size. Our work in addition to providing an automated

Abstract

In this paper, we present an audio-visual model to perform speech super-resolution at large scale-factors (8× and 16×). Previous works attempted to solve this problem using only the audio modality as input and thus were limited to low scale-factors of 2× and 4×. In contrast, we propose to incorporate both visual and auditory signals to superresolve speech of sampling rates as low as 1kHz. In such challenging situations, the visual features assist in learning the content and improves the quality of the generated speech. Further, we demonstrate the applicability of our approach to arbitrary speech signals where the visual stream is not accessible. Our “pseudo-visual network” precisely synthesizes the visual stream solely from the low-resolution speech input. Extensive experiments and the demo video illustrate our method’s remarkable results and benefits over state-of-the-art audio-only speech super-resolution approaches.

Abstract

Communication with the deaf community relies profoundly on the interpretation of sign languages performed by the signers. In light of the recent breakthroughs in sign language translations, we propose a pipeline that we term "Translating Sign Language Videos to Talking Faces". In this context, we improve the existing sign language translation systems by using POS tags to improve language modeling. We further extend the challenge to develop a system that can interpret a video from a signer to an avatar speaking in spoken languages. We focus on the translation systems that attempt to translate sign languages to text without glosses,

Abstract

The recent anomalies in b → slþl− transitions could originate from some new physics beyond the Standard Model. Either to confirm or to rule out this assumption, more tests of b → slþl− transition are needed. Polarized Λb decay to a Λ and a dilepton pair offers a plethora of observables that are suitable to discriminate new physics from the Standard Model. In this paper, we present a full angular analysis of a polarized Λb decay to a Λð→ pπÞlþl− final state. The study is performed in a set of the operator where the Standard Model operator basis is supplemented with its chirality flipped counterparts, and new scalar and pseudoscalar operators. The full angular distribution is calculated by retaining the mass of the final state leptons. At the low hadronic recoil, we use the heavy quark effective theory framework to relate the hadronic form factors which lead to simplified expression of the angular observables where short- and longdistance physics factorize. Using the factorized expressions of the observables, we construct a number of test of short- and long-distance physics including null tests of the Standard Model and its chirality flipped counterparts that can be carried out using experimental data.

Abstract

We explore the possibility of CP violation in baryonic Λb → ðΛþ c ; pþÞπþμ−μ− decays which are mediated by two Majorana sterile neutrinos and are jΔLj ¼ 2 lepton-number-violating processes. Appreciable CP asymmetry can be obtained if there are two on-shell Majorana neutrinos that are quasidegenerate in mass with the mass difference of the order of average decay widths. We find that, given the present constraints on the heavy to light mixing element jVμNj, the Λb → pþπþμ−μ− and Λb → Λþ c πþμ−μ− decay rates are suppressed but could be within the experimental reach at the LHC. If searches of the modes are performed, then experimental limits on the rates can be translated to constraints on the Majorana neutrino mass mN and heavy to light mixing element squared jVμNj 2. We show that the constraints on the ðmN; jVμNj 2Þ parameter space coming from the jΔLj ¼ 2 baryonic decays are complementary to the bounds coming from other processes.

Abstract

Entanglement is a fascinating feature, typical and ubiquitous to the science of quantum information and computation. Over the last few decades, we have seen entanglement, or more precisely bipartite entanglement, playing an unparalleled role in various quantum information-theoretic tasks [1–3]. Multipartite entanglement comes with an even richer structure and a whole new set of features providing information processing advantages in quantum communication, simulation, or metrology. It is therefore essential to construct methods to identify genuinely multipartite entangled states [4–6]. However, detecting entanglement of an arbitrary quantum state is a problem of significant complexity [7, 8]. Thus, witnessing entanglement by using versatile experimentally feasible tools [9–18] is a challenging task. In the case of multipartite entanglement, the challenge enhances significantly and demands greater interest from the scientific community. In this paper, we propose a protocol to detect multipartite entanglement by utilizing the well-known phenomenon of non-Markovianity in open quantum systems [19, 20] and the theory of positive maps. The positive maps that are not completely positive can detect bipartite entanglement quite effectively, Partial Transposition [21] being a well-known example. Unfortunately, this technique cannot be directly extended to the case of multipartite entanglement, where diverse separability structures complicate the detection problem. Although semi-definite programming can be used to construct witnesses to certify genuine multipartite entangle