71 citations · 102 across the 12 of their papers we have counts for
12 papers
MDQE: Mining Discriminative Query Embeddings to Segment Occluded Instances on Challenging Videos
Minghan Li, Shuai Li, Wangmeng Xiang +1
While impressive progress has been achieved, video instance segmentation (VIS) methods with per-clip input often fail on challenging videos with occluded objects and crowded scenes…
One-to-Few Label Assignment for End-to-End Dense Detection
Shuai Li, Minghan Li, Ruihuang Li +2
One-to-one (o2o) label assignment plays a key role for transformer based end-to-end detection, and it has been recently introduced in fully convolutional detectors for end-to-end d…
BoxVIS: Video Instance Segmentation with Box Annotations
Minghan Li, Lei Zhang
It is expensive and labour-extensive to label the pixel-wise object masks in a video. As a result, the amount of pixel-wise annotations in existing video instance segmentation (VIS…
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
Yabin Zhang, Minghan Li, Ruihuang Li +2
Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. Wit…
One-stage Video Instance Segmentation: From Frame-in Frame-out to Clip-in Clip-out
Minghan Li, Lei Zhang
Many video instance segmentation (VIS) methods partition a video sequence into individual frames to detect and segment objects frame by frame. However, such a frame-in frame-out (F…
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics
Thibaut Thonet, Yagmur Gizem Cinar, Eric Gaussier +2
Information retrieval (IR) systems traditionally aim to maximize metrics built on rankings, such as precision or NDCG. However, the non-differentiability of the ranking operation p…
Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation
Minghan Li, Shuai Li, Lida Li +1
Modern one-stage video instance segmentation networks suffer from two limitations. First, convolutional features are neither aligned with anchor boxes nor with ground-truth boundin…
Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures
M. Li, H. Bai, L. Tan +2
Measuring sentence semantic similarity using pre-trained language models such as BERT generally yields unsatisfactory zero-shot performance, and one main reason is ineffective toke…
Learning to Rank for Active Learning: A Listwise Approach
Minghan Li, Xialei Liu, Joost van de Weijer +1
Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous…
A Survey on Rain Removal from Video and Single Image
Hong Wang, Yichen Wu, Minghan Li +2
Rain streaks might severely degenerate the performance of video/image processing tasks. The investigations on rain removal from video or a single image has thus been attracting muc…
Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding
Minghan Li, Xiangyong Cao, Qian Zhao +3
Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of…
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling
Minghan Li, Tanli Zuo, Ruicheng Li +2
Knowledge distillation is an effective technique that transfers knowledge from a large teacher model to a shallow student. However, just like massive classification, large scale kn…