Search Results for author:
Found 27 papers, 15 papers with code
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
The tiny-MOAT family is also benchmarked on downstream tasks, serving as a baseline for the community.
Ranked #28 on
Image Classification
on ImageNet
(using extra training data)
k-means Mask Transformer
However, we observe that most existing transformer-based vision models simply borrow the idea from NLP, neglecting the crucial difference between languages and images, particularly the extremely large sequence length of spatially flattened pixel features.
Ranked #2 on
Panoptic Segmentation
on COCO test-dev
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation
We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering.
Ranked #6 on
Panoptic Segmentation
on COCO test-dev
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
We therefore present the Waymo Open Dataset: Panoramic Video Panoptic Segmentation Dataset, a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.
TubeFormer-DeepLab: Video Mask Transformer
We present TubeFormer-DeepLab, the first attempt to tackle multiple core video segmentation tasks in a unified manner.
Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms
Self-Attention has become prevalent in computer vision models.
DeepLab2: A TensorFlow Library for Deep Labeling
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
We name this joint task as Depth-aware Video Panoptic Segmentation, and propose a new evaluation metric along with two derived datasets for it, which will be made available to the public.
Depth-aware Video Panoptic Segmentation
Monocular Depth Estimation
+2
Batch Normalization with Enhanced Linear Transformation
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data distributions.
Scaling Wide Residual Networks for Panoptic Segmentation
The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant of the Residual Networks (ResNets) by stacking a small number of residual blocks with large channel sizes, have demonstrated outstanding performance on multiple dense prediction tasks.
Ranked #2 on
Panoptic Segmentation
on Cityscapes test
(using extra training data)
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
In this paper, we explore this mechanism in the backbone design for object detection.
Ranked #2 on
Object Detection
on AI-TOD
Deeply Shape-guided Cascade for Instance Segmentation
The key to a successful cascade architecture for precise instance segmentation is to fully leverage the relationship between bounding box detection and mask segmentation across multiple stages.
Rethinking Normalization and Elimination Singularity in Neural Networks
To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models.
TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion
Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
Batch Normalization (BN) has become an out-of-box technique to improve deep network training.
Ranked #61 on
Instance Segmentation
on COCO minival
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
By simply replacing standard optimizers with Neural Rejuvenation, we are able to improve the performances of neural networks by a very large margin while using similar training efforts and maintaining their original resource usages.
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn.
Robust Face Detection via Learning Small Faces on Hard Images
In this paper, we propose that the robustness of a face detector against hard faces can be improved by learning small faces on hard images.
Ranked #7 on
Face Detection
on WIDER Face (Medium)
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
We focus on the problem of training a deep neural network in generations.
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Convolution is spatially-symmetric, i. e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recognition.
Deep Co-Training for Semi-Supervised Image Recognition
We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework.
Unleashing the Potential of CNNs for Interpretable Few-Shot Learning
Our models are based on the idea of encoding objects in terms of visual concepts, which are interpretable visual cues represented by the feature vectors within CNNs.
Single-Shot Object Detection with Enriched Semantics
Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.
Gradually Updated Neural Networks for Large-Scale Image Recognition
Our method is by introducing computation orderings to the channels within convolutional layers or blocks, based on which we gradually compute the outputs in a channel-wise manner.
Few-shot Learning by Exploiting Visual Concepts within CNNs
In this work, we address these limitations of CNNs by developing novel, flexible, and interpretable models for few-shot learning.
Few-Shot Image Recognition by Predicting Parameters from Activations
In this paper, we are interested in the few-shot learning problem.
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.
