Search Results for author:
Found 15 papers, 4 papers with code
MetaFormer Baselines for Vision
By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1K: it achieves an accuracy of 85. 5% at 224x224 resolution, under normal supervised training without external data or distillation.
Ranked #57 on
Image Classification
on ImageNet
(using extra training data)
Federated Zero-Shot Learning with Mid-Level Semantic Knowledge Transfer
To resolve this problem, federated learning has been introduced to transfer knowledge across multiple sources (clients) with non-shared data while optimising a globally generalised central model (server).
Inception Transformer
Recent studies show that Transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information.
Mugs: A Multi-Granular Self-Supervised Learning Framework
It provides complementary instance supervision to IDS via an extra alignment on local neighbors, and scatters different local-groups separately to increase discriminability.
Ranked #2 on
Self-Supervised Image Classification
on ImageNet
Contrastive Learning
Self-Supervised Image Classification
+3
Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption
In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain.
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition
Finally, in the Information Fuser, we explore varied strategies to combine the Sequence Reconstructor and Contrastive Motion Learner, and propose to capture postures and motions simultaneously via a knowledge-distillation based fusion strategy that transfers the motion learning from the Contrastive Motion Learner to the Sequence Reconstructor.
MetaFormer Is Actually What You Need for Vision
Based on this observation, we hypothesize that the general architecture of the Transformers, instead of the specific token mixer module, is more essential to the model's performance.
Ranked #9 on
Semantic Segmentation
on DensePASS
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images
Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class.
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition
Self-supervised learning (SSL) has been proved very effective at learning representations from unlabeled data in the image domain.
Progressive Cluster Purification for Transductive Few-shot Learning
Furthermore, the inter-class classification and the intra-class transduction are extremely flexible to be repeated several times to progressively purify the clusters.
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Nevertheless, how to effectively extract discriminative spatial and temporal features is still a challenging problem.
Ranked #40 on
Skeleton Based Action Recognition
on NTU RGB+D
Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search
To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA).
Multistage Adversarial Losses for Pose-Based Human Image Synthesis
Human image synthesis has extensive practical applications e. g. person re-identification and data augmentation for human pose estimation.
Pose-Based Two-Stream Relational Networks for Action Recognition in Videos
Second, few pose-based methods model the action-related objects in recognizing human-object interaction actions in which objects play an important role.
Action Recognition In Videos
Human-Object Interaction Detection
+1
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning
Skeleton-based action recognition has made great progress recently, but many problems still remain unsolved.
Ranked #63 on
Skeleton Based Action Recognition
on NTU RGB+D
