Hate detection has long been a challenging task for the NLP community. The task becomes complex in a code-mixed environment because the models must understand the context and the hate expressed through language alteration. Compared to the monolingual setup, we see much less work on code-mixed hate as large-scale annotated hate corpora are unavailable for the study. To overcome this bottleneck, we propose using native language hate samples (native language samples/ native samples hereafter). We hypothesise that in the era of multilingual language models (MLMs), hate in code-mixed settings can be detected by majorly relying on the native language samples. Even though the NLP literature reports the effectiveness of MLMs on hate detection in many cross-lingual settings, their extensive evaluation in a code-mixed scenario is yet to be done. This article attempts to fill this gap through rigorous empirical experiments. We considered the Hindi-English code-mixed setup as a case study as we have the linguistic expertise for the same. Some of the interesting observations we got are: (i) adding native hate samples in the code-mixed training set, even in small quantity, improved the performance of MLMs for code-mixed hate detection, (ii) MLMs trained with native samples alone observed to be detecting code-mixed hate to a large extent, (iii) the visualisation of attention scores revealed that, when native samples were included in training, MLMs could better focus on the hate emitting words in the code-mixed context, and (iv) finally, when hate is subjective or sarcastic, naively mixing native samples doesn’t help much to detect code-mixed hate. We have released the data and code repository to reproduce the reported results. 1

3D detection is a critical task that enables machines to identify and locate objects in three-dimensional space. It has a broad range of applications in several fields, including autonomous driving, robotics and augmented reality. Monocular 3D detection is attractive as it requires only a single camera, however, it lacks the accuracy and robustness required for real world applications. High resolution LiDAR on the other hand, can be expensive and lead to interference problems in heavy traffic given their active transmissions. We propose a balanced approach that combines the advantages of monocular and point cloud-based 3D detection. Our method requires only a small number of 3D points, that can be obtained from a low-cost, low-resolution sensor. Specifically, we use only 512 points, which is just 1% of a full LiDAR frame in the KITTI dataset. Our method reconstructs a complete 3D point cloud from this limited 3D information combined with a single image. The reconstructed 3D point cloud and corresponding image can be used by any multi-modal off-the-shelf detector for 3D object detection. By using the proposed network architecture with an off-the-shelf multi-modal 3D detector, the accuracy of 3D detection improves by 20% compared to the state-of-theart monocular detection methods and 6% to 9% compare to the baseline multi-modal methods on KITTI and JackRabbot datasets.

Abstract The swift advancement of convolutional neural networks (CNNs) in numerous real-world utilizations urges an elevation in computational cost along with the size of the model. In this context, many researchers steered their focus to eradicate these specific issues by compressing the original CNN models by pruning weights and filters, respectively. As filter pruning has an upper hand over the weight pruning method because filter pruning methods don’t impact sparse connectivity patterns. In this work, we suggested a Bayesian Convolutional Neural Network (BayesCNN) with Variational Inference, which prefaces probability distribution over weights. For the pruning task of Bayesian CNN, we utilized a combined version of L1-norm with capped L1-norm to help epitomize the amount of information that can be extracted through filter and control regularization. In this formation, we pruned unimportant filters directly without any test accuracy loss and achieved a slimmer model with comparative accuracy. The whole process of pruning is iterative and to validate the performance of our proposed work, we utilized several different CNN architectures on the standard classification dataset available. We have compared our results with non-Bayesian CNN models particularly, datasets such as CIFAR-10 on VGG-16, and pruned 75.8% parameters with float-point-operations (FLOPs) reduction of 51.3% without loss of accuracy and has achieved advancement in state-of-art.

Multiple object tracking in 3D point clouds has applications in mobile robots and autonomous driving. This is a challenging problem due to the sparse nature of the point clouds and the added difficulty of annotation in 3D for supervised learning. To overcome these challenges, we propose a neural network architecture that learns effective object features and their affinities in a self supervised fashion for multiple object tracking in 3D point clouds captured with LiDAR sensors. For self supervision, we use two approaches. First, we generate two augmented LiDAR frames from a single real frame by applying translation, rotation and cutout to the objects. Second, we synthesize a LiDAR frame using CAD models or primitive geometric shapes and then apply the above three augmentations to them. Hence, the ground truth object locations and associations are known in both frames for self supervision. This removes the need to annotate object associations in real data, and additionally the need for training data collection and annotation for object detection in synthetic data. To the best of our knowledge, this is the first self supervised multiple object tracking method for 3D data. Our model achieves state of the art results.