60 citations · 110 across the 10 of their papers we have counts for
10 papers
Making LLaMA SEE and Draw with SEED Tokenizer
Yuying Ge, Sijie Zhao, Ziyun Zeng +4
The great success of Large Language Models (LLMs) has expanded the potential of multimodality, contributing to the gradual evolution of General Artificial Intelligence (AGI). A tru…
GMMFormer: Gaussian-Mixture-Model Based Transformer for Efficient Partially Relevant Video Retrieval
Yuting Wang, Jinpeng Wang, Bin Chen +2
Given a text query, partially relevant video retrieval (PRVR) seeks to find untrimmed videos containing pertinent moments in a database. For PRVR, clip modeling is essential to cap…
Planting a SEED of Vision in Large Language Model
Yuying Ge, Yixiao Ge, Ziyun Zeng +2
We present SEED, an elaborate image tokenizer that empowers Large Language Models (LLMs) with the emergent ability to SEE and Draw at the same time. Research on image tokenizers ha…
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
Jinpeng Wang, Ziyun Zeng, Yunxiao Wang +7
The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommender…
TVTSv2: Learning Out-of-the-box Spatiotemporal Visual Representations at Scale
Ziyun Zeng, Yixiao Ge, Zhan Tong +3
The ultimate goal for foundation models is realizing task-agnostic, i.e., supporting out-of-the-box usage without task-specific fine-tuning. Although breakthroughs have been made i…
Contrastive Masked Autoencoders for Self-Supervised Video Hashing
Yuting Wang, Jinpeng Wang, Bin Chen +2
Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval eff…
Learning Transferable Spatiotemporal Representations from Natural Script Knowledge
Ziyun Zeng, Yuying Ge, Xihui Liu +4
Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years. Despite some progress, existing methods…
Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval
Jinpeng Wang, Bin Chen, Dongliang Liao +4
With the recent boom of video-based social platforms (e.g., YouTube and TikTok), video retrieval using sentence queries has become an important demand and attracts increasing resea…
PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval
Ziyun Zeng, Jinpeng Wang, Bin Chen +3
Deep hashing approaches, including deep quantization and deep binary hashing, have become a common solution to large-scale image retrieval due to their high computation and storage…
Contrastive Quantization with Code Memory for Unsupervised Image Retrieval
Jinpeng Wang, Ziyun Zeng, Bin Chen +2
The high efficiency in computation and storage makes hashing (including binary hashing and quantization) a common strategy in large-scale retrieval systems. To alleviate the relian…