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Found 16 papers, 13 papers with code
PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery
With the recent advancements in single-image-based human mesh recovery, there is a growing interest in enhancing its performance in certain extreme scenarios, such as occlusion, while maintaining overall model accuracy.
Ranked #8 on
3D Human Pose Estimation
on 3DPW
CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification
The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs, focusing on image-text alignment.
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-training
Despite significant advancements in medical vision-language pre-training, existing methods have largely overlooked the inherent entity-specific context within radiology reports and the complex cross-modality contextual relationships between text and images.
TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration
We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities.
OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis
We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses.
AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning
Capitalizing on the recent advancement of 3D-aware GAN models, we perform \emph{guided transfer learning} on a pretrained 3D GAN generator to produce multi-view-consistent stylized renderings.
AvatarGen: A 3D Generative Model for Animatable Human Avatars
Specifically, we decompose the generative 3D human synthesis into pose-guided mapping and canonical representation with predefined human pose and shape, such that the canonical representation can be explicitly driven to different poses and shapes with the guidance of a 3D parametric human model SMPL.
AvatarGen: a 3D Generative Model for Animatable Human Avatars
Unsupervised generation of clothed virtual humans with various appearance and animatable poses is important for creating 3D human avatars and other AR/VR applications.
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
Specifically, we experimentally show that directly encouraging CIL Learner at the initial phase to output similar representations as the model jointly trained on all classes can greatly boost the CIL performance.
VOLO: Vision Outlooker for Visual Recognition
Though recently the prevailing vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification, their performance is still inferior to that of the latest SOTA CNNs if no extra data are provided.
Ranked #1 on
Image Classification
on VizWiz-Classification
Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition
By realizing the importance of the positional information carried by 2D feature representations, unlike recent MLP-like models that encode the spatial information along the flattened spatial dimensions, Vision Permutator separately encodes the feature representations along the height and width dimensions with linear projections.
LV-BERT: Exploiting Layer Variety for BERT
In this paper, beyond this stereotyped layer pattern, we aim to improve pre-trained models by exploiting layer variety from two aspects: the layer type set and the layer order.
Refiner: Refining Self-attention for Vision Transformers
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs.
Ranked #174 on
Image Classification
on ImageNet
All Tokens Matter: Token Labeling for Training Better Vision Transformers
In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs).
Ranked #3 on
Efficient ViTs
on ImageNet-1K (With LV-ViT-S)
DeepViT: Towards Deeper Vision Transformer
In this paper, we show that, unlike convolution neural networks (CNNs)that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper.
Ranked #426 on
Image Classification
on ImageNet
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study.
Ranked #403 on
Image Classification
on ImageNet
