18 citations · 24 across the 5 of their papers we have counts for
7 papers
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment
Abhinav Agarwalla, Abhay Gupta, Alexandre Marques +9
Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accura…
Lidar Panoptic Segmentation and Tracking without Bells and Whistles
Abhinav Agarwalla, Xuhua Huang, Jason Ziglar +5
State-of-the-art lidar panoptic segmentation (LPS) methods follow bottom-up segmentation-centric fashion wherein they build upon semantic segmentation networks by utilizing cluster…
Completely Self-Supervised Crowd Counting via Distribution Matching
Deepak Babu Sam, Abhinav Agarwalla, Jimmy Joseph +3
Dense crowd counting is a challenging task that demands millions of head annotations for training models. Though existing self-supervised approaches could learn good representation…
Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach
Avisek Lahiri, Abhinav Agarwalla, Prabir Kumar Biswas
With contemporary advancements of graphics engines, recent trend in deep learning community is to train models on automatically annotated simulated examples and apply on real data…
Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images
Abhinav Agarwalla, Muhammad Shaban, Nasir M. Rajpoot
Convolutional Neural Network (CNN) models have become the state-of-the-art for most computer vision tasks with natural images. However, these are not best suited for multi-gigapixe…
Recurrent Memory Addressing for describing videos
Arnav Kumar Jain, Abhinav Agarwalla, Kumar Krishna Agrawal +1
In this paper, we introduce Key-Value Memory Networks to a multimodal setting and a novel key-addressing mechanism to deal with sequence-to-sequence models. The proposed model natu…
Bayesian Optimisation with Prior Reuse for Motion Planning in Robot Soccer
Abhinav Agarwalla, Arnav Kumar Jain, KV Manohar +2
We integrate learning and motion planning for soccer playing differential drive robots using Bayesian optimisation. Trajectories generated using end-slope cubic Bezier splines are…