Search Results for author:
Found 30 papers, 10 papers with code
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation
We also present a method which injects the style representation of the web-crawled data into the source domain on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training.
Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?
Specifically, we replace the MLP module in the token-mixing step with a novel sparse MLP (sMLP) module.
Ranked #131 on
Image Classification
on ImageNet
Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration
Given a piece of speech and its transcript text, text-based speech editing aims to generate speech that can be seamlessly inserted into the given speech by editing the transcript.
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP
Convolutional neural networks (CNN) are the dominant deep neural network (DNN) architecture for computer vision.
Self-Supervised Visual Representations Learning by Contrastive Mask Prediction
In this paper, we propose a novel contrastive mask prediction (CMP) task for visual representation learning and design a mask contrast (MaskCo) framework to implement the idea.
VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild
We estimate 3D poses from the voxel representation by predicting whether each voxel contains a particular body joint.
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification
Occluded person re-identification (ReID) aims to match person images with occlusion.
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow
In membership/subscriber acquisition and retention, we sometimes need to recommend marketing content for multiple pages in sequence.
ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation
We study what features should be aligned across domains and propose to make the domain alignment proactively serve classification by performing feature decomposition and alignment under the guidance of the prior knowledge induced from the classification taskitself.
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning
In this work, we propose a novel method, dubbed PlayVirtual, which augments cycle-consistent virtual trajectories to enhance the data efficiency for RL feature representation learning.
Understanding Mobile GUI: from Pixel-Words to Screen-Sentences
A dataset, RICO-PW, of screenshots with Pixel-Words annotations is built based on the public RICO dataset, which will be released to help to address the lack of high-quality training data in this area.
Unsupervised Visual Representation Learning by Tracking Patches in Video
The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame.
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domaininvariant structural representation from an image by disentangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation.
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification
Each recomposed feature, obtained based on the domain-invariant feature (which enables a reliable inheritance of identity) and an enhancement from a domain specific feature (which enables the approximation of real distributions), is thus an "ideal" augmentation.
Domain Adaptive Person Re-Identification
Person Re-Identification
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation
For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning or by explicitly aligning their statistics.
Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation
Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a domain discriminator in order to have aligned feature distributions.
Generalizing to Unseen Domains: A Survey on Domain Generalization
Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.
Do Generative Models Know Disentanglement? Contrastive Learning is All You Need
DisCo consists of: (i) a Navigator providing traversal directions in the latent space, and (ii) a $\Delta$-Contrastor composed of two shared-weight Encoders, which encode image pairs along these directions to disentangled representations respectively, and a difference operator to map the encoded representations to the Variation Space.
Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement
From the unsupervised disentanglement perspective, we rethink content and style and propose a formulation for unsupervised C-S disentanglement based on our assumption that different factors are of different importance and popularity for image reconstruction, which serves as a data bias.
GroupifyVAE: from Group-based Definition to VAE-based Unsupervised Representation Disentanglement
The key idea of the state-of-the-art VAE-based unsupervised representation disentanglement methods is to minimize the total correlation of the latent variable distributions.
AttributeNet: Attribute Enhanced Vehicle Re-Identification
Vehicle Re-Identification (V-ReID) is a critical task that associates the same vehicle across images from different camera viewpoints.
Ranked #1 on
Vehicle Re-Identification
on VeRi-Wild Small
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework
This paper presents a self-supervised learning framework, named MGF, for general-purpose speech representation learning.
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild
We believe that VAE$^2$ is also applicable to other stochastic sequence prediction problems where training data are lack of stochasticity.
Style Normalization and Restitution for Domain Generalization and Adaptation
In this paper, we design a novel Style Normalization and Restitution module (SNR) to simultaneously ensure both high generalization and discrimination capability of the networks.
Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification
Based on this finding, we propose to exploit the uncertainty (measured by consistency levels) to evaluate the reliability of the pseudo-label of a sample and incorporate the uncertainty to re-weight its contribution within various ReID losses, including the identity (ID) classification loss per sample, the triplet loss, and the contrastive loss.
Domain Adaptive Person Re-Identification
Person Re-Identification
+1
An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation
The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations.
Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric
In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i. e.}, all returned images are considered as the target.
AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild
To make the task truly unconstrained, we present AdaFuse, an adaptive multiview fusion method, which can enhance the features in occluded views by leveraging those in visible views.
Uncertainty-Aware Few-Shot Image Classification
In this work, we propose Uncertainty-Aware Few-Shot framework for image classification by modeling uncertainty of the similarities of query-support pairs and performing uncertainty-aware optimization.
FPCR-Net: Feature Pyramidal Correlation and Residual Reconstruction for Semi-supervised Optical Flow Estimation
To exploit such flexible and comprehensive information, we propose a semi-supervised Feature Pyramidal Correlation and Residual Reconstruction Network (FPCR-Net) for optical flow estimation from frame pairs.