Search Results for author:
Found 6 papers, 3 papers with code
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding
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
However, most models do not offer controllability to manipulate the shape semantics of component object parts without extensive semantic attribute labels or other reference point clouds.
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification
The pre-trained semantic feature extractor (learned from a large-scale text corpora) we use in our approach provides a strong contextual prior knowledge to assist FSL.
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
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 structure of point clouds.
3D Object Classification
3D Point Cloud Linear Classification
+3
Towards Accurate Cross-Domain In-Bed Human Pose Estimation
Majority of the contactless human pose estimation algorithms are based on RGB modality, causing ineffectiveness in in-bed pose estimation due to occlusions by blankets and varying illumination conditions.
Rich Semantics Improve Few-shot Learning
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).
Ranked #1 on
Few-Shot Image Classification
on Oxford 102 Flower
(using extra training data)
