5 citations · 12 across the 19 of their papers we have counts for
20 papers
Accelerating Diffusion Language Models via Structured Suffix Modeling
Zifeng Cheng, Keda Li, Zhiwei Jiang +3
Diffusion Language Models (DLMs) exhibit strong parallel decoding capabilities by denoising multiple tokens in a single generation step. However, this parallelism comes with substa…
Dynamic Distribution-Aware Uncertainty Tracking in Vision-Language Representation Learning
Ao Zhou, Zhiwei Jiang, Zifeng Cheng +4
Uncertainty Quantification (UQ) aims to measure the reliability of model predictions, serving as a critical safeguard for deploying Vision-Language Models (VLMs) in safety-critical…
ScaleErasure: Inference-Time Minimal Intervention for Precise Concept Erasure in Next-Scale Autoregressive Image Generation
Cong Wang, Haiyu Wu, Zhiwei Jiang +4
Concept erasure aims to prevent image generative models from producing unsafe content while preserving their general generative capability. Meanwhile, next-scale autoregressive (AR…
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…
Steering When Necessary: Flexible Steering Large Language Models with Backtracking
Zifeng Cheng, Jinwei Gan, Zhiwei Jiang +5
Large language models (LLMs) have achieved remarkable performance across many generation tasks. Nevertheless, effectively aligning them with desired behaviors remains a significant…
Hierarchical Vision-Language Reasoning for Multimodal Multiple-Choice Question Answering
Ao Zhou, Zebo Gu, Tenghao Sun +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable multimodal understanding capabilities in Visual Question Answering (VQA) tasks by integrating visual and textu…
Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction
Ao Zhou, Mingsheng Tu, Luping Wang +5
Social Media Popularity Prediction is a complex multimodal task that requires effective integration of images, text, and structured information. However, current approaches suffer…
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…
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Zifeng Cheng, Zhonghui Wang, Yuchen Fu +4
Extracting sentence embeddings from large language models (LLMs) is a practical direction, as it requires neither additional data nor fine-tuning. Previous studies usually focus on…
Graph Network for Sign Language Tasks
Shiwei Gan, Yafeng Yin, Zhiwei Jiang +3
Recent advances in sign language research have benefited from CNN-based backbones, which are primarily transferred from traditional computer vision tasks (\eg object identification…
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…
Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs
Yuchen Fu, Zifeng Cheng, Zhiwei Jiang +4
Extracting sentence embeddings from large language models (LLMs) is a promising direction, as LLMs have demonstrated stronger semantic understanding capabilities. Previous studies…
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models
Zhiyuan Hu, Yuliang Liu, Jinman Zhao +8
Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts t…
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…
V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation
Cong Wang, Kuan Tian, Jun Zhang +7
In the field of portrait video generation, the use of single images to generate portrait videos has become increasingly prevalent. A common approach involves leveraging generative…
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…
Ensembling Diffusion Models via Adaptive Feature Aggregation
Cong Wang, Kuan Tian, Yonghang Guan +4
The success of the text-guided diffusion model has inspired the development and release of numerous powerful diffusion models within the open-source community. These models are typ…
Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
Zifeng Cheng, Qingyu Zhou, Zhiwei Jiang +3
Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or s…
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…
Learning to Classify Open Intent via Soft Labeling and Manifold Mixup
Zifeng Cheng, Zhiwei Jiang, Yafeng Yin +2
Open intent classification is a practical yet challenging task in dialogue systems. Its objective is to accurately classify samples of known intents while at the same time detectin…