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Found 20 papers, 11 papers with code
DGMamba: Domain Generalization via Generalized State Space Model
SPR strives to encourage the model to concentrate more on objects rather than context, consisting of two designs: Prior-Free Scanning~(PFS), and Domain Context Interchange~(DCI).
Test-Time Domain Generalization for Face Anti-Spoofing
Our method, consisting of Test-Time Style Projection (TTSP) and Diverse Style Shifts Simulation (DSSS), effectively projects the unseen data to the seen domain space.
SimAda: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes
In this paper, we aim to investigate the impact of the general vision modules on finetuning SAM and enable them to generalize across all downstream tasks.
Continuous Piecewise-Affine Based Motion Model for Image Animation
To address this issue, we propose to model motion from the source image to the driving frame in highly-expressive diffeomorphism spaces.
BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model
To this end, we propose Scalable Bias-Mode Attention Mask (BA-SAM) to enhance SAM's adaptability to varying image resolutions while eliminating the need for structure modifications.
Rethinking Domain Generalization: Discriminability and Generalizability
On the one hand, the simultaneous attainment of generalizability and discriminability of features presents a complex challenge, often entailing inherent contradictions.
Diverse Target and Contribution Scheduling for Domain Generalization
In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains.
Instance-Aware Domain Generalization for Face Anti-Spoofing
To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels.
Rethinking Implicit Neural Representations for Vision Learners
Existing INRs methods suffer from two problems: 1) narrow theoretical definitions of INRs are inapplicable to high-level tasks; 2) lack of representation capabilities to deep networks.
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing
Existing DG-based FAS approaches always capture the domain-invariant features for generalizing on the various unseen domains.
Generative Domain Adaptation for Face Anti-Spoofing
Most existing UDA FAS methods typically fit the trained models to the target domain via aligning the distribution of semantic high-level features.
TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers
Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors.
Ranked #4 on
Video Object Detection
on ImageNet VID
(using extra training data)
Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
In this paper, we propose a novel and fully end-to-end trainable approach, called regional contrastive consistency regularization (RCCR) for domain adaptive semantic segmentation.
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
Extensive experiments demonstrate that our method can soundly boost the performance on both cross-domain object detection and segmentation for state-of-the-art techniques.
Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling
Inspired by this, we propose a novel semi-supervised framework based on pseudo-labeling for outdoor 3D object detection tasks.
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels.
Ranked #5 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Self-Adversarial Disentangling for Specific Domain Adaptation
In this paper, we study a practical setting called Specific Domain Adaptation (SDA) that aligns the source and target domains in a demanded-specific dimension.
Image-to-Image Translation on Cityscapes-to-Foggy Cityscapes
object-detection
+3
End-to-End Video Object Detection with Spatial-Temporal Transformers
Recently, DETR and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors.
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
Guided by this mask, we propose a ClassOut strategy to realize effective regional consistency in a fine-grained manner.
DMT: Dynamic Mutual Training for Semi-Supervised Learning
Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors.
