5 citations · 12 across the 10 of their papers we have counts for
11 papers
SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses
Chaolei Tan, Zihang Lin, Junfu Pu +7
Video grounding is a fundamental problem in multimodal content understanding, aiming to localize specific natural language queries in an untrimmed video. However, current video gro…
PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
Chen Li, Xutan Peng, Teng Wang +5
Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently. However,…
Knowledge-augmented Few-shot Visual Relation Detection
Tianyu Yu, Yangning Li, Jiaoyan Chen +8
Visual Relation Detection (VRD) aims to detect relationships between objects for image understanding. Most existing VRD methods rely on thousands of training samples of each relati…
Binary Embedding-based Retrieval at Tencent
Yukang Gan, Yixiao Ge, Chang Zhou +7
Large-scale embedding-based retrieval (EBR) is the cornerstone of search-related industrial applications. Given a user query, the system of EBR aims to identify relevant informatio…
Darwinian Model Upgrades: Model Evolving with Selective Compatibility
Binjie Zhang, Shupeng Su, Yixiao Ge +5
The traditional model upgrading paradigm for retrieval requires recomputing all gallery embeddings before deploying the new model (dubbed as "backfilling"), which is quite expensiv…
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval
Shupeng Su, Binjie Zhang, Yixiao Ge +4
The task of privacy-preserving model upgrades in image retrieval desires to reap the benefits of rapidly evolving new models without accessing the raw gallery images. A pioneering…
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification
Yu Zhang, Zhihong Shen, Chieh-Han Wu +5
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set. Most existing LMTC approaches rely on massive h…
Towards Universal Backward-Compatible Representation Learning
Binjie Zhang, Yixiao Ge, Yantao Shen +6
Conventional model upgrades for visual search systems require offline refresh of gallery features by feeding gallery images into new models (dubbed as "backfill"), which is time-co…
Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval
Binjie Zhang, Yixiao Ge, Yantao Shen +5
The task of hot-refresh model upgrades of image retrieval systems plays an essential role in the industry but has never been investigated in academia before. Conventional cold-refr…
3rd Place: A Global and Local Dual Retrieval Solution to Facebook AI Image Similarity Challenge
Xinlong Sun, Yangyang Qin, Xuyuan Xu +3
As a basic task of computer vision, image similarity retrieval is facing the challenge of large-scale data and image copy attacks. This paper presents our 3rd place solution to the…
Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation
Yujia Zhang, Lai-Man Po, Xuyuan Xu +5
Spatio-temporal representation learning is critical for video self-supervised representation. Recent approaches mainly use contrastive learning and pretext tasks. However, these ap…