output
20202026
most citedSolve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration

90 citations

Showing 2022Show all

7 papers · 1 filter

cs.CV2022★ 90 cited

Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration

Liqi Yan, Qifan Wang, Siqi Ma +2

Instance segmentation in videos, which aims to segment and track multiple objects in video frames, has garnered a flurry of research attention in recent years. In this paper, we pr…

cs.IR2022★ 89 cited

DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation

Yifang Qin, Yifan Wang, Fang Sun +6

Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geograph…

cs.IR2022★ 5 cited

Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension

Siyu Yuan, Deqing Yang, Jiaqing Liang +5

The concepts in knowledge graphs (KGs) enable machines to understand natural language, and thus play an indispensable role in many applications. However, existing KGs have the poor…

cs.CL2022★ 12 cited

Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations

Qian Yang, Yunxin Li, Baotian Hu +3

Visual Entailment with natural language explanations aims to infer the relationship between a text-image pair and generate a sentence to explain the decision-making process. Previo…

cs.RO2022★ 7 cited

Point Cloud Change Detection With Stereo V-SLAM:Dataset, Metrics and Baseline

Zihan Lin, Jincheng Yu, Lipu Zhou +3

Localization and navigation are basic robotic tasks requiring an accurate and up-to-date map to finish these tasks, with crowdsourced data to detect map changes posing an appealing…

cs.IR2022★ 27 cited

Debiasing Learning for Membership Inference Attacks Against Recommender Systems

Zihan Wang, Na Huang, Fei Sun +5

Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems…