no code implementations • 3 Oct 2023 • Tushar Choudhary, Vikrant Dewangan, Shivam Chandhok, Shubham Priyadarshan, Anushka Jain, Arun K. Singh, Siddharth Srivastava, Krishna Murthy Jatavallabhula, K. Madhava Krishna
Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts.
no code implementations • 6 Sep 2022 • Rohith Rajesh, Sumit J. Darak, Akshay Jain, Shivam Chandhok, Animesh Sharma
The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and FPGA.
no code implementations • 13 Jun 2022 • Puneet Mangla, Shivam Chandhok, Milan Aggarwal, Vineeth N Balasubramanian, Balaji Krishnamurthy
To this end, we propose IntriNsic multimodality for DomaIn GeneralizatiOn (INDIGO), a simple and elegant way of leveraging the intrinsic modality present in these pre-trained multimodal networks along with the visual modality to enhance generalization to unseen domains at test-time.
1 code implementation • CVPR 2022 • Hari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok, Vineeth N Balasubramanian
Secondly, we introduce a unified feature-generative framework for CGZSL that leverages bi-directional incremental alignment to dynamically adapt to addition of new classes, with or without labeled data, that arrive over time in any of these CGZSL settings.
no code implementations • 23 Jul 2021 • Piyush Sahoo, Romesh Rajoria, Shivam Chandhok, S. J. Darak, Danilo Pau, Hem-Dutt Dabral
With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains.
no code implementations • 15 Jul 2021 • Puneet Mangla, Shivam Chandhok, Vineeth N Balasubramanian, Fahad Shahbaz Khan
Recent progress towards designing models that can generalize to unseen domains (i. e domain generalization) or unseen classes (i. e zero-shot learning) has embarked interest towards building models that can tackle both domain-shift and semantic shift simultaneously (i. e zero-shot domain generalization).
no code implementations • 12 Jul 2021 • Shivam Chandhok, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Vineeth N Balasubramanian, Fahad Shahbaz Khan, Ling Shao
The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain generalization (DG), to separately address the issues of semantic shift and domain shift, respectively.
no code implementations • 11 Jul 2021 • Gaurav Bhatt, Shivam Chandhok, Vineeth N Balasubramanian
In this work, we present a practical setting of inductive zero and few-shot learning, where unlabeled images from other out-of-data classes, that do not belong to seen or unseen categories, can be used to improve generalization in any-shot learning.
no code implementations • 15 Jul 2020 • Shivam Chandhok, Vineeth N. Balasubramanian
The performance of generative zero-shot methods mainly depends on the quality of generated features and how well the model facilitates knowledge transfer between visual and semantic domains.
no code implementations • 11 Dec 2019 • Shivam Chandhok, Himani Joshi, A. V. Subramanyam, Sumit J. Darak
Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.)