266 citations · 593 across the 98 of their papers we have counts for
109 papers · 1 filter
M3GA-Wild: A Large-Scale Dataset and Benchmark for Multi-Modal Multi-session Ground-to-Aerial Place Recognition in Forests
Ethan Griffiths, Maryam Haghighat, Simon Denman +2
We present M3GA-Wild, the first benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests. M3GA-Wild unifies and extends existing forest localisation d…
Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers
Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando +6
Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in many safety-critical system…
TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation
Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy +4
Cross-modal distillation from Vision Foundation Models (VFMs) to LiDAR backbones has recently emerged as a self-supervised pretraining strategy that reduces reliance on dense point…
Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splatting
Binh Long Nguyen, Kien Nguyen, Sridha Sridharan +2
We introduce Ilov3Splat, a novel framework for instance-level open-vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D-GS). Most prior work depends on 2D rendering…
OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism
Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh +2
Generalized Category Discovery (GCD) challenges methods to identify known and novel classes using partially labeled data, mirroring human category learning. Unlike prior GCD method…
DIS2: Disentanglement Meets Distillation with Classwise Attention for Robust Remote Sensing Segmentation under Missing Modalities
Nhi Kieu, Kien Nguyen, Arnold Wiliem +2
The efficacy of multimodal learning in remote sensing (RS) is severely undermined by missing modalities. The challenge is exacerbated by the RS highly heterogeneous data and huge s…