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Found 27 papers, 12 papers with code
Balancing Act: Distribution-Guided Debiasing in Diffusion Models
We train Attribute Distribution Predictor (ADP) - a small mlp that maps the latent features to the distribution of attributes.
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation
Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain.
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
We are the first to utilize vision transformers for domain adaptation in a privacy-oriented source-free setting, and our approach achieves state-of-the-art performance on single-source, multi-source, and multi-target benchmarks
Subsidiary Prototype Alignment for Universal Domain Adaptation
Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift.
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation
The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
Balancing Discriminability and Transferability for Source-Free Domain Adaptation
Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discriminability knowledge gathered from the labeled source data.
Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
To this end, we cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target.
Ranked #5 on
Unsupervised 3D Human Pose Estimation
on Human3.6M
Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery
Articulation-centric 2D/3D pose supervision forms the core training objective in most existing 3D human pose estimation techniques.
Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation
The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations.
Ranked #8 on
Unsupervised 3D Human Pose Estimation
on Human3.6M
Monocular 3D Human Pose Estimation
Unsupervised 3D Human Pose Estimation
+2
Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation
However, we find that latent features derived from the Fourier-based amplitude spectrum of deep CNN features hold a more tractable mapping with domain discrimination.
Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation (DA) has gained substantial interest in semantic segmentation.
Ranked #4 on
Domain Generalization
on GTA5-to-Cityscapes
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Thus, we aim to utilize implicit alignment without additional training objectives to perform adaptation.
Domain Adaptation
Multi-Source Unsupervised Domain Adaptation
Appearance Consensus Driven Self-Supervised Human Mesh Recovery
We present a self-supervised human mesh recovery framework to infer human pose and shape from monocular images in the absence of any paired supervision.
Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation
Our approach not only generalizes to in-the-wild images, but also yields a superior trade-off between speed and performance, compared to prior top-down approaches.
Class-Incremental Domain Adaptation
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA).
Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation
However, generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable, as these models often perform unsatisfactorily on unseen in-the-wild environments.
Cross-Conditioned Recurrent Networks for Long-Term Synthesis of Inter-Person Human Motion Interactions
Modeling dynamics of human motion is one of the most challenging sequence modeling problem, with diverse applications in animation industry, human-robot interaction, motion-based surveillance, etc.
Universal Source-Free Domain Adaptation
1) In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift.
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis
Camera captured human pose is an outcome of several sources of variation.
Towards Inheritable Models for Open-Set Domain Adaptation
Addressing this, we introduce a practical DA paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future.
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation
In this paper, we propose UM-Adapt - a unified framework to effectively perform unsupervised domain adaptation for spatially-structured prediction tasks, simultaneously maintaining a balanced performance across individual tasks in a multi-task setting.
GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions
Despite the remarkable success of generative adversarial networks, their performance seems less impressive for diverse training sets, requiring learning of discontinuous mapping functions.
BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN
The discriminator is trained also to regress this extrinsic factor r, which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence.
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
In this work we propose a novel temporal pose-sequence modeling framework, which can embed the dynamics of 3D human-skeleton joints to a continuous latent space in an efficient manner.
Object Pose Estimation from Monocular Image using Multi-View Keypoint Correspondence
In this work, we propose a data-efficient method which utilizes the geometric regularity of intraclass objects for pose estimation.
iSPA-Net: Iterative Semantic Pose Alignment Network
Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation.
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation
Supervised deep learning methods have shown promising results for the task of monocular depth estimation; but acquiring ground truth is costly, and prone to noise as well as inaccuracies.
