Search Results for author:
Found 8 papers, 4 papers with code
Tell, Don't Show!: Language Guidance Eases Transfer Across Domains in Images and Videos
We introduce LaGTran, a novel framework that utilizes readily available or easily acquired text descriptions to guide robust transfer of discriminative knowledge from labeled source to unlabeled target data with domain shifts.
GeoNet: Benchmarking Unsupervised Adaptation across Geographies
In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training.
Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision
Many top-down architectures for instance segmentation achieve significant success when trained and tested on pre-defined closed-world taxonomy.
Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels
Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes.
MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptation
Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone cannot handle sufficiently well.
Fine-Grained Visual Recognition
Unsupervised Domain Adaptation
Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation
Domain adaptation deals with training models using large scale labeled data from a specific source domain and then adapting the knowledge to certain target domains that have few or no labels.
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation
A majority of methods for video frame interpolation compute bidirectional optical flow between adjacent frames of a video, followed by a suitable warping algorithm to generate the output frames.
Ranked #2 on
Video Frame Interpolation
on GoPro
Universal Semi-Supervised Semantic Segmentation
In recent years, the need for semantic segmentation has arisen across several different applications and environments.
Ranked #27 on
Semantic Segmentation
on DensePASS
(using extra training data)
