Search Results for author:
Found 33 papers, 18 papers with code
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning
In this work, we show that Relation Graph augmented Learning (RGL) can improve the performance of few-shot natural language understanding tasks.
StageInteractor: Query-based Object Detector with Cross-stage Interaction
Most of these detectors are trained with one-to-many label assignment strategies.
PDPP:Projected Diffusion for Procedure Planning in Instructional Videos
In this sense, we model the whole intermediate action sequence distribution with a diffusion model (PDPP), and thus transform the planning problem to a sampling process from this distribution.
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications.
CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets
Our CoMAE presents a curriculum learning strategy to unify the two popular self-supervised representation learning algorithms: contrastive learning and masked image modeling.
Learning 3D-aware Image Synthesis with Unknown Pose Distribution
Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution pre-estimated on the training set.
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery
Intelligent fault diagnosis is essential to safe operation of machinery.
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation
Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping.
Cross-Architecture Self-supervised Video Representation Learning
In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning.
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning
Previous methods achieved good performance on such manipulation tasks.
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
However, the optimal video summaries need to reflect the most valuable keyframe with its own information, and one with semantic power of the whole content.
AdaMixer: A Fast-Converging Query-Based Object Detector
The recent query-based object detectors break this convention by decoding image features with a set of learnable queries.
Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents
To make DCSC fully utilize the limited known intents, we propose a two-stage training procedure for DCSC, in which DCSC will be trained on both labeled samples and unlabeled samples, and achieve better text representation and clustering performance.
InsCLR: Improving Instance Retrieval with Self-Supervision
This work aims at improving instance retrieval with self-supervision.
A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark
In this paper, we aim to present an in-depth study on few-shot video classification by making three contributions.
WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification
We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet).
Mutually-aware Sub-Graphs Differentiable Architecture Search
In this paper, we propose a conceptually simple yet efficient method to bridge these two paradigms, referred as Mutually-aware Sub-Graphs Differentiable Architecture Search (MSG-DAS).
Unchain the Search Space with Hierarchical Differentiable Architecture Search
To overcome this limitation, in this paper, we propose a Hierarchical Differentiable Architecture Search (H-DAS) that performs architecture search both at the cell level and at the stage level.
Representation Sharing for Fast Object Detector Search and Beyond
FAD consists of a designed search space and an efficient architecture search algorithm.
Wavelet Integrated CNNs for Noise-Robust Image Classification
The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets.
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data.
V4D: 4D Convolutional Neural Networks for Video-level Representation Learning
Most existing 3D CNN structures for video representation learning are clip-based methods, and do not consider video-level temporal evolution of spatio-temporal features.
Knowledge Integration Networks for Action Recognition
We design a three-branch architecture consisting of a main branch for action recognition, and two auxiliary branches for human parsing and scene recognition which allow the model to encode the knowledge of human and scene for action recognition.
V4D:4D Convolutional Neural Networks for Video-level Representation Learning
Most existing 3D CNNs for video representation learning are clip-based methods, and thus do not consider video-level temporal evolution of spatio-temporal features.
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation
Weakly-supervised instance segmentation aims to detect and segment object instances precisely, given imagelevel labels only.
Ranked #7 on
Image-level Supervised Instance Segmentation
on PASCAL VOC 2012 val
(using extra training data)
General Classification
Image-level Supervised Instance Segmentation
+5
The iMaterialist Fashion Attribute Dataset
The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled raw from the Internet by using text queries, without any human annotation.
Ranked #1 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2.
Locally-Supervised Deep Hybrid Model for Scene Recognition
Secondly, we propose a new Local Convolutional Supervision (LCS) layer to enhance the local structure of the image by directly propagating the label information to the convolutional layers.
Better Exploiting OS-CNNs for Better Event Recognition in Images
Event recognition from still images is one of the most important problems for image understanding.
Places205-VGGNet Models for Scene Recognition
We verify the performance of trained Places205-VGGNet models on three datasets: MIT67, SUN397, and Places205.
Local Color Contrastive Descriptor for Image Classification
Our descriptor enriches local image representation with both color and contrast information.
