Search Results for author:
Found 13 papers, 8 papers with code
Residual Learning for Image Point Descriptors
Such an approach of optimization allows us to discard learning knowledge already present in non-differentiable functions such as the hand-crafted descriptors and only learn the residual knowledge in the main network branch.
Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences
Our proposed method enhances cross-domain localization performance, significantly reducing the performance gap.
Breathing New Life into 3D Assets with Generative Repainting
Diffusion-based text-to-image models ignited immense attention from the vision community, artists, and content creators.
Composite Learning for Robust and Effective Dense Predictions
In this paper, we find that jointly training a dense prediction (target) task with a self-supervised (auxiliary) task can consistently improve the performance of the target task, while eliminating the need for labeling auxiliary tasks.
Ranked #102 on
Semantic Segmentation
on NYU Depth v2
ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization
Efficient detection and description of geometric regions in images is a prerequisite in visual systems for localization and mapping.
Efficient Visual Tracking with Exemplar Transformers
E. T. Track, our visual tracker that incorporates Exemplar Transformer modules, runs at 47 FPS on a CPU.
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance.
Exploring Relational Context for Multi-Task Dense Prediction
Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations.
Ranked #78 on
Semantic Segmentation
on NYU Depth v2
Task Switching Network for Multi-Task Learning
Multiple tasks are performed by switching between them, performing one task at a time.
Automated Search for Resource-Efficient Branched Multi-Task Networks
The multi-modal nature of many vision problems calls for neural network architectures that can perform multiple tasks concurrently.
Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference
First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning).
T-Basis: a Compact Representation for Neural Networks
Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks.
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing
Face anti-spoofing is a measure towards this direction for bio-metric user authentication, and in particular face recognition, that tries to prevent spoof attacks.
