👋 Hi there! Welcome to my website ✨

Note: I am currently looking for internship opportunities. Please feel free to contact me if you have any relevant positions available.

I am a joint PhD student at the University of Surrey (UoS), UK, and the University of Wollongong (UoW), Australia, working on resource- and data-efficient methods for 3D scene understanding. My research is supervised by Xiatian Zhu at UoS and Son Lam Phung at UoW, in collaboration with Lu Yin and Salim Bouzerdoum.

Before embarking on my PhD journey, I worked at MBZUAI in Abu Dhabi, where I collaborated with Salman Khan and Fahad Khan on utilising foundational models for continual learning in image and point cloud understanding.

I completed my undergraduate studies at SGGS Institute of Engineering and Technology, Nanded in India. During my undergrad, I had the opportunity to spend six months at Nanyang Technological University (NTU), Singapore.

My research interests include:

• 3D computer vision
• Continual learning
• Multi-modal learning
• Efficient learning

I am open to collaboration. Feel free to connect with me for further discussion.

🔥 News

📝 Publications

A full publication list is available on my Google Scholar page.

BMVC 2025

CLIMB-3D: Continual Learning for Imbalanced 3D Instance Segmentation

Vishal Thengane, Jean Lahoud, Hisham Cholakkal, Rao Muhammad Anwer, Lu Yin, Xiatian Zhu, Salman Khan

We introduce class-incremental learning for point cloud instance segmentation and curate benchmarks from the long-tail ScanNet200 dataset. To address class imbalance, we propose a novel module that ensures more uniform performance across frequent and rare classes.

arXiv GitHub Project

CVPR 2023

CLIP Model is an Efficient Continual Learner

Vishal Thengane, Salman Khan, Munawar Hayat, Fahad Khan

This work demonstrates that a frozen CLIP model, evaluated in zero-shot mode, achieves SOTA performance across multiple continual learning settings without any fine-tuning. Tested on five benchmarks, CLIP surpasses existing methods while avoiding re-training, memory replay, or architectural tweaks, making it a strong and surprisingly simple baseline for future CL research.

arXiv GitHub Project

ECCV 2022

Strong Gravitational Lensing Parameter Estimation with Vision Transformer

Kuan-Wei Huang, Geoff Chih-Fan Chen, Po-Wen Chang, Sheng-Chieh Lin, Chia-Jung Hsu, Vishal Thengane, Joshua Yao-Yu Lin

We explore Vision Transformers (ViTs) for estimating parameters in simulated lensed quasar systems—offering a fast, competitive alternative to MCMC and CNNs. ViTs perform well on mass-related lensing parameters, showing promise for future lensing analyses.

arXiv GitHub

Pre-prints

Arxiv 2025

Foundational Models for 3D Point Clouds: A Survey and Outlook

Vishal Thengane, Xiatian Zhu, Salim Bouzerdoum, Son Lam Phung, Yunpeng Li

This paper surveys recent advances in foundation models for 3D point cloud understanding, focusing on how 2D and language-based pretrained models help overcome challenges like limited labelled data and high computational costs. It reviews strategies for building 3D FMs, their application across core 3D tasks, and highlights future research directions.

arXiv GitHub

Arxiv 2024

Gradient Correlation Subspace Learning against Catastrophic Forgetting

Vishal Thengane, Tammuz Dubnov

This paper proposes Gradient Correlation Subspace Learning (GCSL) to address catastrophic forgetting in incremental class learning. GCSL identifies and preserves weight subspaces least affected by prior tasks, projecting new task updates into them, and can be flexibly applied across network layers and tasks.

arXiv GitHub

🎖 Honours and Awards

• 🏆 Best Paper Award, European Conference on Computer Vision (ECCV), 2022
• ✈️ Alan Turing Mobility Grant, August 2024 – December 2025

📖 Educations

University of Surrey (UK) & University of Wollongong (Australia)

Joint PhD, Computer Science
Oct 2023 – Present

• Research: Few-shot & Incremental 3D Scene Understanding
• Focus: Vision-Language Models, Efficient Learning, 3D Computer Vision

SGGS Institute of Engineering and Technology Nanded, India

B.Tech, Electronics and Telecommunication Engineering
Jul 2015 – May 2019

• Graduated with First Class Honours
• Relevant Coursework: Deep Learning, Computer Vision, Data Structures, Robotics