44 citations · 85 across the 6 of their papers we have counts for
7 papers · 1 filter
Towards Improving Calibration in Object Detection Under Domain Shift
Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz +1
With deep neural network based solution more readily being incorporated in real-world applications, it has been pressing requirement that predictions by such models, especially in…
Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz +1
We study adapting trained object detectors to unseen domains manifesting significant variations of object appearance, viewpoints and backgrounds. Most current methods align domains…
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
M. Saquib Sarfraz, Naila Murray, Vivek Sharma +3
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. For this…
Clustering based Contrastive Learning for Improving Face Representations
Vivek Sharma, Makarand Tapaswi, M. Saquib Sarfraz +1
A good clustering algorithm can discover natural groupings in data. These groupings, if used wisely, provide a form of weak supervision for learning representations. In this work,…
Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss
M. Saquib Sarfraz, Constantin Seibold, Haroon Khalid +1
Generative adversarial networks has emerged as a defacto standard for image translation problems. To successfully drive such models, one has to rely on additional networks e.g., di…
Self-Supervised Learning of Face Representations for Video Face Clustering
Vivek Sharma, Makarand Tapaswi, M. Saquib Sarfraz +1
Analyzing the story behind TV series and movies often requires understanding who the characters are and what they are doing. With improving deep face models, this may seem like a s…