79 citations · 200 across the 20 of their papers we have counts for
24 papers
EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels
Kunyu Peng, Di Wen, Kailun Yang +9
Open-Set Domain Generalization (OSDG) aims to enable deep learning models to recognize unseen categories in new domains, which is crucial for real-world applications. Label noise h…
Is Visual in-Context Learning for Compositional Medical Tasks within Reach?
Simon Reiß, Zdravko Marinov, Alexander Jaus +4
In this paper, we explore the potential of visual in-context learning to enable a single model to handle multiple tasks and adapt to new tasks during test time without re-training.…
Go Beyond Earth: Understanding Human Actions and Scenes in Microgravity Environments
Di Wen, Lei Qi, Kunyu Peng +9
Despite substantial progress in video understanding, most existing datasets are limited to Earth's gravitational conditions. However, microgravity alters human motion, interactions…
OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection
David Schneider, Zdravko Marinov, Moritz Mistol +6
Visual fall detection models are usually trained on small, staged datasets. Their real-world utility remains unclear; such data lacks diversity and evaluation protocols differ from…
Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations
David Schneider, Simon Reiß, Marco Kugler +6
Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attribut…
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler
Kunyu Peng, Di Wen, Kailun Yang +6
In Open-Set Domain Generalization (OSDG), the model is exposed to both new variations of data appearance (domains) and open-set conditions, where both known and novel categories ar…
Referring Atomic Video Action Recognition
Kunyu Peng, Jia Fu, Kailun Yang +8
We introduce a new task called Referring Atomic Video Action Recognition (RAVAR), aimed at identifying atomic actions of a particular person based on a textual description and the…
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir +2
In this work, we tackle the problem of domain generalization for object detection, specifically focusing on the scenario where only a single source domain is available. We propose…
AltChart: Enhancing VLM-based Chart Summarization Through Multi-Pretext Tasks
Omar Moured, Jiaming Zhang, M. Saquib Sarfraz +1
Chart summarization is a crucial task for blind and visually impaired individuals as it is their primary means of accessing and interpreting graphical data. Crafting high-quality d…
Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation
Ruiping Liu, Jiaming Zhang, Kunyu Peng +6
Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory fra…
Navigating Open Set Scenarios for Skeleton-based Action Recognition
Kunyu Peng, Cheng Yin, Junwei Zheng +7
In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones. Howeve…
Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning
Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz +1
Deep learning based object detectors struggle generalizing to a new target domain bearing significant variations in object and background. Most current methods align domains by usi…
Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains
Kunyu Peng, Di Wen, David Schneider +5
Domain adaptation is essential for activity recognition to ensure accurate and robust performance across diverse environments, sensor types, and data sources. Unsupervised domain a…
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…
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
Constantin Seibold, Simon Reiß, M. Saquib Sarfraz +2
When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined c…
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…
A Multimodal Assistive System for Helping Visually Impaired in Social Interactions
M. Saquib Sarfraz, Angela Constantinescu, Melanie Zuzej +1
Access to non-verbal cues in social interactions is vital for people with visual impairment. It has been shown that non-verbal cues such as eye contact, number of people, their nam…
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
M. Saquib Sarfraz, Arne Schumann, Andreas Eberle +1
Person re identification is a challenging retrieval task that requires matching a person's acquired image across non overlapping camera views. In this paper we propose an effective…
Deep Perceptual Mapping for Cross-Modal Face Recognition
M. Saquib Sarfraz, Rainer Stiefelhagen
Cross modal face matching between the thermal and visible spectrum is a much desired capability for night-time surveillance and security applications. Due to a very large modality…
Deep Perceptual Mapping for Thermal to Visible Face Recognition
M. Saquib Sarfraz, Rainer Stiefelhagen
Cross modal face matching between the thermal and visible spectrum is a much de- sired capability for night-time surveillance and security applications. Due to a very large modalit…