Search Results for author:
Found 13 papers, 3 papers with code
MeTA: Multi-source Test Time Adaptation
Test time adaptation is the process of adapting, in an unsupervised manner, a pre-trained source model to each incoming batch of the test data (i. e., without requiring a substantial portion of the test data to be available, as in traditional domain adaptation) and without access to the source data.
STRIDE: Single-video based Temporally Continuous Occlusion Robust 3D Pose Estimation
This challenge arises because these models struggle to generalize beyond their training datasets, and the variety of occlusions is hard to capture in the training data.
POISE: Pose Guided Human Silhouette Extraction under Occlusions
Human silhouette extraction is a fundamental task in computer vision with applications in various downstream tasks.
Effective Restoration of Source Knowledge in Continual Test Time Adaptation
By restoring the knowledge from the source, it effectively corrects the negative consequences arising from the gradual deterioration of model parameters caused by ongoing shifts in the domain.
Prior-guided Source-free Domain Adaptation for Human Pose Estimation
To address this limitation, we focus on the task of source-free domain adaptation for pose estimation, where a source model must adapt to a new target domain using only unlabeled target data.
SUMMIT: Source-Free Adaptation of Uni-Modal Models to Multi-Modal Targets
In this work, we relax both of these assumptions by addressing the problem of adapting a set of models trained independently on uni-modal data to a target domain consisting of unlabeled multi-modal data, without having access to the original source dataset.
Controllable Dynamic Multi-Task Architectures
In contrast to the existing dynamic multi-task approaches that adjust only the weights within a fixed architecture, our approach affords the flexibility to dynamically control the total computational cost and match the user-preferred task importance better.
Reconstruction guided Meta-learning for Few Shot Open Set Recognition
However, it is a critical task in many applications like environmental monitoring, where the number of labeled examples for each class is limited.
Cross-domain Imitation from Observations
Once this correspondence is found, we can directly transfer the demonstrations on one domain to the other and use it for imitation.
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain from a single source model without requiring access to the source data.
Exploiting Temporal Coherence for Self-Supervised One-shot Video Re-identification
While supervised techniques in re-identification are extremely effective, the need for large amounts of annotations makes them impractical for large camera networks.
Learning Person Re-identification Models from Videos with Weak Supervision
In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.
Multiple Instance Learning
Video-Based Person Re-Identification
Channel masking for multivariate time series shapelets
Time series shapelets are discriminative sub-sequences and their similarity to time series can be used for time series classification.
