This paper studies the problem of traffic flow forecasting, which aims t...
We tackle the data scarcity challenge in few-shot point cloud recognitio...
Finding abnormal lymph nodes in radiological images is highly important ...
We show that classifiers trained with random region proposals achieve
st...
Although graph neural networks (GNNs) have achieved impressive achieveme...
In this paper, we investigate the challenge of spatio-temporal video
pre...
Existing knowledge distillation works for semantic segmentation mainly f...
Few-shot semantic segmentation is the task of learning to locate each pi...
Out-Of-Distribution generalization (OOD) is all about learning invarianc...
Conventional de-noising methods rely on the assumption that all samples ...
Transformer-based methods have recently achieved great advancement on 2D...
Since Intersection-over-Union (IoU) based optimization maintains the
con...
Leveraging StyleGAN's expressivity and its disentangled latent codes,
ex...
Semi-Supervised Learning (SSL) is fundamentally a missing label problem,...
Semi-supervised object detection (SSOD) aims to facilitate the training ...
Weakly supervised object detection (WSOD), which is an effective way to ...
Monocular 3D object detection is an essential task in autonomous driving...
The application of cross-dataset training in object detection tasks is
c...
Cross-modality interaction is a critical component in Text-Video Retriev...
Extracting class activation maps (CAM) is arguably the most standard ste...
We address the overlooked unbiasedness in existing long-tailed classific...
Unsupervised Person Re-identification (U-ReID) with pseudo labeling rece...
Spinal degeneration plagues many elders, office workers, and even the yo...
Bounding box (bbox) regression is a fundamental task in computer vision....
Finding a suitable density function is essential for density-based clust...
Though 3D object detection from point clouds has achieved rapid progress...
Current geometry-based monocular 3D object detection models can efficien...
Existing Unsupervised Domain Adaptation (UDA) literature adopts the cova...
Knowledge Distillation (KD) is a popular technique to transfer knowledge...
With the increasing scale and diversification of interaction behaviors i...
Recently, some contrastive learning methods have been proposed to
simult...
Despite their success for semantic segmentation, convolutional neural
ne...
Cloth-Changing person re-identification (CC-ReID) aims at matching the s...
Video-based person re-identification (re-ID) aims at matching the same p...
We propose a causal framework to explain the catastrophic forgetting in
...
We present a novel counterfactual framework for both Zero-Shot Learning ...
This paper tackles the purely unsupervised person re-identification (Re-...
Model efficiency is crucial for object detection. Mostprevious works rel...
Skeleton-based human action recognition has attracted much attention wit...
Multi-object tracking (MOT) has always been a very important research
di...
Recently, hashing is widely-used in approximate nearest neighbor search ...
We uncover an ever-overlooked deficiency in the prevailing Few-Shot Lear...
Today, scene graph generation(SGG) task is largely limited in realistic
...
With the rise of deep learning methods, person Re-Identification (ReID)
...
Recently, many unsupervised deep learning methods have been proposed to ...
Due to the existence of label noise in web images and the high memorizat...
How to learn a stable model under agnostic distribution shift between
tr...
Due to the advanced capabilities of the Internet of Vehicles (IoV) compo...
Based on the framework of multiple instance learning (MIL), tremendous w...
Despite significant progress of applying deep learning methods to the fi...