Search Results for author:
Found 30 papers, 19 papers with code
Global entity alignment with Gated Latent Space Neighborhood Aggregation
In the paper we propose a global entity alignment model with gated latent space neighborhood aggregation (LatsEA) to address this challenge.
C^3KG: A Chinese Commonsense Conversation Knowledge Graph
Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.
Unleashing the Power of Visual Prompting At the Pixel Level
This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks.
SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training
Coupling all these designs allows our method to enjoy both competitive performances on text-to-video retrieval and video question answering tasks, and much less pre-training costs by 1. 9X or more.
Masked Autoencoders Enable Efficient Knowledge Distillers
For example, by distilling the knowledge from an MAE pre-trained ViT-L into a ViT-B, our method achieves 84. 0% ImageNet top-1 accuracy, outperforming the baseline of directly distilling a fine-tuned ViT-L by 1. 2%.
High-Resolution Swin Transformer for Automatic Medical Image Segmentation
Most of the existing Transformer-based networks for medical image segmentation are U-Net-like architecture that contains an encoder that utilizes a sequence of Transformer blocks to convert the input medical image from high-resolution representation into low-resolution feature maps and a decoder that gradually recovers the high-resolution representation from low-resolution feature maps.
In Defense of Image Pre-Training for Spatiotemporal Recognition
Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels.
C3KG: A Chinese Commonsense Conversation Knowledge Graph
Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.
CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation
Recent advances in self-supervised contrastive learning yield good image-level representation, which favors classification tasks but usually neglects pixel-level detailed information, leading to unsatisfactory transfer performance to dense prediction tasks such as semantic segmentation.
Masked Feature Prediction for Self-Supervised Visual Pre-Training
We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models.
Ranked #2 on
Action Recognition
on AVA v2.2
(using extra training data)
Embedding Decomposition for Artifacts Removal in EEG Signals
DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal.
Phase function estimation from a diffuse optical image via deep learning
The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters.
iBOT: Image BERT Pre-Training with Online Tokenizer
We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer.
Ranked #1 on
Unsupervised Image Classification
on ImageNet
Playing for 3D Human Recovery
Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios.
Image BERT Pre-training with Online Tokenizer
The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces.
Towards an Online Empathetic Chatbot with Emotion Causes
Existing emotion-aware conversational models usually focus on controlling the response contents to align with a specific emotion class, whereas empathy is the ability to understand and concern the feelings and experience of others.
Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization
EEG source localization is an important technical issue in EEG analysis.
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning
Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied.
Writing Polishment with Simile: Task, Dataset and A Neural Approach
In this paper, we propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do.
Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension
Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.
Design and verification of the HXI collimator onboard the ASO-S mission
The HXI collimator (HXI-C) is a spatial modulation X-ray telescope designed to observe hard X-rays emitted by energetic electrons in solar flares.
Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics High Energy Physics - Experiment
Self-supervised Representation Learning for Evolutionary Neural Architecture Search
To enhance the predictive performance of neural predictors, we devise two self-supervised learning methods from different perspectives to pre-train the architecture embedding part of neural predictors to generate a meaningful representation of neural architectures.
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
Regarding the similarity of the query crop to each crop from other images as "unlabeled", the consistency term takes the corresponding similarity of a positive crop as a pseudo label, and encourages consistency between these two similarities.
Focus-Constrained Attention Mechanism for CVAE-based Response Generation
To model diverse responses for a given post, one promising way is to introduce a latent variable into Seq2Seq models.
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing deep learning-based denoising models, as well as for performance comparisons across models.
NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search
In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors.
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning
We consider spatial contexts, for which we solve so-called jigsaw puzzles, i. e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration.
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.
Deep Retinex Decomposition for Low-Light Enhancement
Based on the decomposition, subsequent lightness enhancement is conducted on illumination by an enhancement network called Enhance-Net, and for joint denoising there is a denoising operation on reflectance.
Ranked #5 on
Low-Light Image Enhancement
on DICM
