Search Results for author:
Found 9 papers, 4 papers with code
iEdit: Localised Text-guided Image Editing with Weak Supervision
We propose a novel learning method for text-guided image editing, namely \texttt{iEdit}, that generates images conditioned on a source image and a textual edit prompt.
Contrastive Language-Action Pre-training for Temporal Localization
Long-form video understanding requires designing approaches that are able to temporally localize activities or language.
Contrastive Learning
Few Shot Temporal Action Localization
+3
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects.
Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning
Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models.
Ranked #6 on
Cross-Modal Retrieval
on Recipe1M
GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss
We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes.
Shape Reconstruction by Learning Differentiable Surface Representations
Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations.
Quadruplet Selection Methods for Deep Embedding Learning
Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification.
GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping
We fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions.
Good Features to Correlate for Visual Tracking
The proposed learning framework enables the network model to be flexible for a custom design.
Ranked #2 on
Visual Object Tracking
on VOT2016
