papers

Publications (7)

cs.CV2025

Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization

Cong Wang, Zexuan Deng, Zhiwei Jiang +6

Sign Language Video Generation (SLVG) seeks to generate identity-preserving sign language videos from spoken language texts. Existing methods primarily rely on the single coarse co…

cs.CV2023

Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning

Cong Wang, Zhiwei Jiang, Yafeng Yin +3

For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes. Intuit…

cs.CL2024

A Debiased Nearest Neighbors Framework for Multi-Label Text Classification

Zifeng Cheng, Zhiwei Jiang, Yafeng Yin +5

Multi-Label Text Classification (MLTC) is a practical yet challenging task that involves assigning multiple non-exclusive labels to each document. Previous studies primarily focus…

cs.CL2025

RegionMarker: A Region-Triggered Semantic Watermarking Framework for Embedding-as-a-Service Copyright Protection

Shufan Yang, Zifeng Cheng, Zhiwei Jiang +5

Embedding-as-a-Service (EaaS) is an effective and convenient deployment solution for addressing various NLP tasks. Nevertheless, recent research has shown that EaaS is vulnerable t…

cs.CV2025

MUVR: A Multi-Modal Untrimmed Video Retrieval Benchmark with Multi-Level Visual Correspondence

Yue Feng, Jinwei Hu, Qijia Lu +11

We propose the Multi-modal Untrimmed Video Retrieval task, along with a new benchmark (MUVR) to advance video retrieval for long-video platforms. MUVR aims to retrieve untrimmed vi…

cs.CL2025

Multi-Prompting Decoder Helps Better Language Understanding

Zifeng Cheng, Zhaoling Chen, Zhiwei Jiang +4

Recent Pre-trained Language Models (PLMs) usually only provide users with the inference APIs, namely the emerging Model-as-a-Service (MaaS) setting. To adapt MaaS PLMs to downstrea…

cs.CV2025

Implicit Location-Caption Alignment via Complementary Masking for Weakly-Supervised Dense Video Captioning

Shiping Ge, Qiang Chen, Zhiwei Jiang +4

Weakly-Supervised Dense Video Captioning (WSDVC) aims to localize and describe all events of interest in a video without requiring annotations of event boundaries. This setting pos…