activity
20212025
most citedCal-DETR: Calibrated Detection Transformer

4 citations · 10 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV20251 cited

CrossVideoMAE: Self-Supervised Image-Video Representation Learning with Masked Autoencoders

Shihab Aaqil Ahamed, Malitha Gunawardhana, Liel David +3

Current video-based Masked Autoencoders (MAEs) primarily focus on learning effective spatiotemporal representations from a visual perspective, which may lead the model to prioritiz…

cs.RO2025

GazeGrasp: DNN-Driven Robotic Grasping with Wearable Eye-Gaze Interface

Issatay Tokmurziyev, Miguel Altamirano Cabrera, Luis Moreno +2

We present GazeGrasp, a gaze-based manipulation system enabling individuals with motor impairments to control collaborative robots using eye-gaze. The system employs an ESP32 CAM f…

cs.CV20241 cited

Towards Generalizing to Unseen Domains with Few Labels

Chamuditha Jayanga Galappaththige, Sanoojan Baliah, Malitha Gunawardhana +1

We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically, our aim is to obtain a model that learns domain-generalizable features by levera…

cs.CV20234 cited

Cal-DETR: Calibrated Detection Transformer

Muhammad Akhtar Munir, Salman Khan, Muhammad Haris Khan +2

Albeit revealing impressive predictive performance for several computer vision tasks, deep neural networks (DNNs) are prone to making overconfident predictions. This limits the ado…

cs.CV20232 cited

Unsupervised Landmark Discovery Using Consistency Guided Bottleneck

Mamona Awan, Muhammad Haris Khan, Sanoojan Baliah +4

We study a challenging problem of unsupervised discovery of object landmarks. Many recent methods rely on bottlenecks to generate 2D Gaussian heatmaps however, these are limited in…

cs.CV2023

Multiclass Alignment of Confidence and Certainty for Network Calibration

Vinith Kugathasan, Muhammad Haris Khan

Deep neural networks (DNNs) have made great strides in pushing the state-of-the-art in several challenging domains. Recent studies reveal that they are prone to making overconfiden…