Search Results for author:
Found 16 papers, 7 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 #59 on
Image Classification
on ImageNet
(using extra training data)
Robust Distillation for Worst-class Performance
We show empirically that our robust distillation techniques not only achieve better worst-class performance, but also lead to Pareto improvement in the tradeoff between overall performance and worst-class performance compared to other baseline methods.
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
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
Malicious Mode Attack on EV Coordinated Charging Load and MIADRC Defense Strategy
Further, to mitigate the impact of MMA, a defense strategy based on multi-index information active disturbance rejection control is proposed to improve the stability and anti-disturbance ability of the power system, which considers the impact factors of both mode damping and disturbance compensation.
Distilling Double Descent
Distillation is the technique of training a "student" model based on examples that are labeled by a separate "teacher" model, which itself is trained on a labeled dataset.
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
In machine learning applications such as ranking fairness or fairness over intersectional groups, one often encounters optimization problems with an extremely large number of constraints.
A Simple Baseline for Pose Tracking in Videos of Crowded Scenes
This paper presents our solution to ACM MM challenge: Large-scale Human-centric Video Analysis in Complex Events\cite{lin2020human}; specifically, here we focus on Track3: Crowd Pose Tracking in Complex Events.
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes
In this paper, we focus on improving human pose estimation in videos of crowded scenes from the perspectives of exploiting temporal context and collecting new data.
Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes
Prior works always fail to deal with this problem in two aspects: (1) lacking utilizing information of the scenes; (2) lacking training data in the crowd and complex scenes.
Tree Boosted Varying Coefficient Models
This paper investigates the integration of gradient boosted decision trees and varying coefficient models.
Methodology
A Deep-Learning-Based Fashion Attributes Detection Model
Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections.
Approximation Trees: Statistical Stability in Model Distillation
Here, we consider the use of regression trees as a student model, in which nodes of the tree can be used as `explanations' for particular predictions, and the whole structure of the tree can be used as a global representation of the resulting function.
Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution
This paper examines a novel gradient boosting framework for regression.
Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition
Accurate and computationally efficient means for classifying human activities have been the subject of extensive research efforts.
