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Found 15 papers, 8 papers with code
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.
Open Vocabulary Object Detection with Proposal Mining and Prediction Equalization
Open-vocabulary object detection (OVD) aims to scale up vocabulary size to detect objects of novel categories beyond the training vocabulary.
Ranked #18 on
Open Vocabulary Object Detection
on LVIS v1.0
Efficient Decoder-free Object Detection with Transformers
A natural usage of ViTs in detection is to replace the CNN-based backbone with a transformer-based backbone, which is straightforward and effective, with the price of bringing considerable computation burden for inference.
Training-free Transformer Architecture Search
Recently, Vision Transformer (ViT) has achieved remarkable success in several computer vision tasks.
ARM: Any-Time Super-Resolution Method
To that effect, we construct an Edge-to-PSNR lookup table that maps the edge score of an image patch to the PSNR performance for each subnet, together with a set of computation costs for the subnets.
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Vision transformers (ViTs) have recently received explosive popularity, but the huge computational cost is still a severe issue.
Ranked #11 on
Efficient ViTs
on ImageNet-1K (with DeiT-T)
Dual Reweighting Domain Generalization for Face Presentation Attack Detection
Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios.
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing
Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios.
Towards Corruption-Agnostic Robust Domain Adaptation
However, due to unpredictable corruptions (e. g., noise and blur) in real data like web images, domain adaptation methods are increasingly required to be corruption robust on target domains.
On Evolving Attention Towards Domain Adaptation
However, considering that the configuration of attention, i. e., the type and the position of attention module, affects the performance significantly, it is more generalized to optimize the attention configuration automatically to be specialized for arbitrary UDA scenario.
Ranked #1 on
Partial Domain Adaptation
on Office-Home
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation
For intra-domain propagation, we propose an effective self-training strategy to mitigate the noises in pseudo-labeled target domain data and improve the feature discriminability in the target domain.
Dynamic Refinement Network for Oriented and Densely Packed Object Detection
However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning
In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective.
Gourmet Photography Dataset for Aesthetic Assessment of Food Images
In this study, we present the Gourmet Photography Dataset (GPD), which is the first large-scale dataset for aesthetic assessment of food photographs.
Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment
Aggregation structures with explicit information, such as image attributes and scene semantics, are effective and popular for intelligent systems for assessing aesthetics of visual data.
Ranked #1 on
Aesthetics Quality Assessment
on AVA
