21 citations · 36 across the 9 of their papers we have counts for
9 papers
Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos
Mohamed Afham, Christoph Reich, Oliver Hahn +2
Procedure planning seeks to estimate a sequence of actions to transition from an observed initial state to a given goal state. Current procedure planning approaches directly predic…
CrossJEPA: Cross-Modal Joint-Embedding Predictive Architecture for Efficient 3D Representation Learning from 2D Images
Avishka Perera, Kumal Hewagamage, Saeedha Nazar +4
Image-to-point cross-modal learning has emerged to address the scarcity of large-scale 3D datasets in 3D representation learning. However, current methods that leverage 2D data oft…
A Feature Generator for Few-Shot Learning
Heethanjan Kanagalingam, Thenukan Pathmanathan, Navaneethan Ketheeswaran +3
Few-shot learning (FSL) aims to enable models to recognize novel objects or classes with limited labelled data. Feature generators, which synthesize new data points to augment limi…
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding
Mohamed Afham, Satya Narayan Shukla, Omid Poursaeed +3
While most modern video understanding models operate on short-range clips, real-world videos are often several minutes long with semantically consistent segments of variable length…
3DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulation
Amaya Dharmasiri, Dinithi Dissanayake, Mohamed Afham +3
3D generative models have been recently successful in generating realistic 3D objects in the form of point clouds. However, most models do not offer controllability to manipulate t…
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification
Mohamed Afham, Ranga Rodrigo
Few-Shot learning aims to train and optimize a model that can adapt to unseen visual classes with only a few labeled examples. The existing few-shot learning (FSL) methods, heavily…
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake +3
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular struct…
Towards Accurate Cross-Domain In-Bed Human Pose Estimation
Mohamed Afham, Udith Haputhanthri, Jathurshan Pradeepkumar +3
Human behavioral monitoring during sleep is essential for various medical applications. Majority of the contactless human pose estimation algorithms are based on RGB modality, caus…
Rich Semantics Improve Few-shot Learning
Mohamed Afham, Salman Khan, Muhammad Haris Khan +2
Human learning benefits from multi-modal inputs that often appear as rich semantics (e.g., description of an object's attributes while learning about it). This enables us to learn…