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Found 17 papers, 13 papers with code
Multimodal Label Relevance Ranking via Reinforcement Learning
Conventional multi-label recognition methods often focus on label confidence, frequently overlooking the pivotal role of partial order relations consistent with human preference.
Fingerprint Presentation Attack Detector Using Global-Local Model
While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained.
Text-Guided 3D Face Synthesis - From Generation to Editing
In the editing stage we first employ a pre-trained diffusion model to update facial geometry or texture based on the texts.
Text-Guided 3D Face Synthesis -- From Generation to Editing
In the editing stage, we first employ a pre-trained diffusion model to update facial geometry or texture based on the texts.
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images.
Ranked #2 on
Surface Normals Estimation
on Stanford-ORB
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies
Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations.
Improving GAN Training via Feature Space Shrinkage
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
AdaptiveMix: Improving GAN Training via Feature Space Shrinkage
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
Towards Unbiased Volume Rendering of Neural Implicit Surfaces With Geometry Priors
In this paper, we argue that this limited accuracy is due to the bias of their volume rendering strategies, especially when the viewing direction is close to be tangent to the surface.
NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation
Temporal video segmentation is the get-to-go automatic video analysis, which decomposes a long-form video into smaller components for the following-up understanding tasks.
Decoupled Mixup for Generalized Visual Recognition
Since the observation is that noise-prone regions such as textural and clutter backgrounds are adverse to the generalization ability of CNN models during training, we enhance features from discriminative regions and suppress noise-prone ones when combining an image pair.
Combating Mode Collapse in GANs via Manifold Entropy Estimation
In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs.
See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval
To explore the fine-grained alignment, we further propose two implicit semantic alignment paradigms: multi-level alignment (MLA) and bidirectional mask modeling (BMM).
Robust Representation via Dynamic Feature Aggregation
With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks.
Scene Consistency Representation Learning for Video Scene Segmentation
Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods.
Manifold-preserved GANs
Generative Adversarial Networks (GANs) have been widely adopted in various fields.
Group-wise Inhibition based Feature Regularization for Robust Classification
The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e. g. corrupted and adversarial samples).
Ranked #40 on
Domain Generalization
on ImageNet-C
