128 citations · 724 across the 72 of their papers we have counts for
59 papers · 1 filter
3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization
Yuze Hao, Linchao Zhu, Yi Yang
Inverse design aims to design the input variables of a physical system to optimize a specified objective function, typically formulated as a search or optimization problem. However…
FlexSelect: Flexible Token Selection for Efficient Long Video Understanding
Yunzhu Zhang, Yu Lu, Tianyi Wang +3
Long-form video understanding poses a significant challenge for video large language models (VideoLLMs) due to prohibitively high computational and memory demands. In this paper, w…
From Trial to Triumph: Advancing Long Video Understanding via Visual Context Sample Scaling and Self-reward Alignment
Yucheng Suo, Fan Ma, Linchao Zhu +3
Multi-modal Large language models (MLLMs) show remarkable ability in video understanding. Nevertheless, understanding long videos remains challenging as the models can only process…
VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing
Xiangpeng Yang, Linchao Zhu, Hehe Fan +1
Recent advancements in diffusion models have significantly improved video generation and editing capabilities. However, multi-grained video editing, which encompasses class-level,…
Noise-Tolerant Hybrid Prototypical Learning with Noisy Web Data
Chao Liang, Linchao Zhu, Zongxin Yang +2
We focus on the challenging problem of learning an unbiased classifier from a large number of potentially relevant but noisily labeled web images given only a few clean labeled ima…
MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs
Yunqiu Xu, Linchao Zhu, Yi Yang
While multimodal large language models (MLLMs) have demonstrated extraordinary vision-language understanding capabilities, their abilities to solve instance-level visual-language p…
FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention
Yu Lu, Yuanzhi Liang, Linchao Zhu +1
Video diffusion models have made substantial progress in various video generation applications. However, training models for long video generation tasks require significant computa…
AudioScenic: Audio-Driven Video Scene Editing
Kaixin Shen, Ruijie Quan, Linchao Zhu +2
Audio-driven visual scene editing endeavors to manipulate the visual background while leaving the foreground content unchanged, according to the given audio signals. Unlike current…
EVA: Zero-shot Accurate Attributes and Multi-Object Video Editing
Xiangpeng Yang, Linchao Zhu, Hehe Fan +1
Current diffusion-based video editing primarily focuses on local editing (\textit{e.g.,} object/background editing) or global style editing by utilizing various dense correspondenc…
Knowledge-Enhanced Dual-stream Zero-shot Composed Image Retrieval
Yucheng Suo, Fan Ma, Linchao Zhu +1
We study the zero-shot Composed Image Retrieval (ZS-CIR) task, which is to retrieve the target image given a reference image and a description without training on the triplet datas…
CapHuman: Capture Your Moments in Parallel Universes
Chao Liang, Fan Ma, Linchao Zhu +2
We concentrate on a novel human-centric image synthesis task, that is, given only one reference facial photograph, it is expected to generate specific individual images with divers…
DGL: Dynamic Global-Local Prompt Tuning for Text-Video Retrieval
Xiangpeng Yang, Linchao Zhu, Xiaohan Wang +1
Text-video retrieval is a critical multi-modal task to find the most relevant video for a text query. Although pretrained models like CLIP have demonstrated impressive potential in…
FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax
Yu Lu, Linchao Zhu, Hehe Fan +1
Text-to-video (T2V) generation is a rapidly growing research area that aims to translate the scenes, objects, and actions within complex video text into a sequence of coherent visu…
Text Augmented Spatial-aware Zero-shot Referring Image Segmentation
Yucheng Suo, Linchao Zhu, Yi Yang
In this paper, we study a challenging task of zero-shot referring image segmentation. This task aims to identify the instance mask that is most related to a referring expression wi…
Combating Label Noise With A General Surrogate Model For Sample Selection
Chao Liang, Linchao Zhu, Humphrey Shi +1
Modern deep learning systems are data-hungry. Learning with web data is one of the feasible solutions, but will introduce label noise inevitably, which can hinder the performance o…
DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion
Yunhong Lou, Linchao Zhu, Yaxiong Wang +2
We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions while preserving motion diversity.Despite the recent signif…
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition
Chao Liang, Zongxin Yang, Linchao Zhu +1
In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in…
Gloss-Free End-to-End Sign Language Translation
Kezhou Lin, Xiaohan Wang, Linchao Zhu +3
In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations. Although intermediate representation like gloss has been proven effective, gloss…
Efficient Multimodal Fusion via Interactive Prompting
Yaowei Li, Ruijie Quan, Linchao Zhu +1
Large-scale pre-training has brought unimodal fields such as computer vision and natural language processing to a new era. Following this trend, the size of multi-modal learning mo…
Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models
Shuai Zhao, Xiaohan Wang, Linchao Zhu +1
One fascinating aspect of pre-trained vision-language models~(VLMs) learning under language supervision is their impressive zero-shot generalization capability. However, this abili…
CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
Shuai Zhao, Ruijie Quan, Linchao Zhu +1
Pre-trained vision-language models~(VLMs) are the de-facto foundation models for various downstream tasks. However, scene text recognition methods still prefer backbones pre-traine…
DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training
Wei Li, Linchao Zhu, Longyin Wen +1
Large-scale pre-trained multi-modal models (e.g., CLIP) demonstrate strong zero-shot transfer capability in many discriminative tasks. Their adaptation to zero-shot image-condition…
Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding
Juncheng Li, Siliang Tang, Linchao Zhu +5
Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vis…
Temporal Perceiving Video-Language Pre-training
Fan Ma, Xiaojie Jin, Heng Wang +4
Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve g…