6 citations · 17 across the 14 of their papers we have counts for
12 papers · 1 filter
Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning
Chendi Ge, Xin Wang, Zeyang Zhang +5
Continual multimodal instruction tuning is crucial for adapting Multimodal Large Language Models (MLLMs) to evolving tasks. However, most existing methods adopt a fixed architectur…
Modular-Cam: Modular Dynamic Camera-view Video Generation with LLM
Zirui Pan, Xin Wang, Yipeng Zhang +4
Text-to-Video generation, which utilizes the provided text prompt to generate high-quality videos, has drawn increasing attention and achieved great success due to the development…
VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding
Houlun Chen, Xin Wang, Hong Chen +5
Existing Video Corpus Moment Retrieval (VCMR) is limited to coarse-grained understanding, which hinders precise video moment localization when given fine-grained queries. In this p…
Multi-sentence Video Grounding for Long Video Generation
Wei Feng, Xin Wang, Hong Chen +2
Video generation has witnessed great success recently, but their application in generating long videos still remains challenging due to the difficulty in maintaining the temporal c…
DisenStudio: Customized Multi-subject Text-to-Video Generation with Disentangled Spatial Control
Hong Chen, Xin Wang, Yipeng Zhang +4
Generating customized content in videos has received increasing attention recently. However, existing works primarily focus on customized text-to-video generation for single subjec…
Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos
Houlun Chen, Xin Wang, Hong Chen +3
Temporal Sentence Grounding (TSG), which aims to localize moments from videos based on the given natural language queries, has attracted widespread attention. Existing works are ma…
LLM4VG: Large Language Models Evaluation for Video Grounding
Wei Feng, Xin Wang, Hong Chen +7
Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models. However, the ability of LLMs to handle video gr…
VTimeLLM: Empower LLM to Grasp Video Moments
Bin Huang, Xin Wang, Hong Chen +2
Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details. Ho…
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search
Siao Tang, Xin Wang, Hong Chen +3
Diffusion models have recently shown remarkable generation ability, achieving state-of-the-art performance in many tasks. However, the high computational cost is still a troubling…
Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing
Siao Tang, Xin Wang, Hong Chen +4
High computational overhead is a troublesome problem for diffusion models. Recent studies have leveraged post-training quantization (PTQ) to compress diffusion models. However, mos…
VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning on Language-Video Foundation Models
Hong Chen, Xin Wang, Guanning Zeng +5
Customized text-to-video generation aims to generate text-guided videos with user-given subjects, which has gained increasing attention. However, existing works are primarily limit…
Revisiting Transformation Invariant Geometric Deep Learning: An Initial Representation Perspective
Ziwei Zhang, Xin Wang, Zeyang Zhang +2
Deep neural networks have achieved great success in the last decade. When designing neural networks to handle the ubiquitous geometric data such as point clouds and graphs, it is c…