2 citations · 6 across the 11 of their papers we have counts for
12 papers
Omni-WorldBench: Towards a Comprehensive Interaction-Centric Evaluation for World Models
Meiqi Wu, Zhixin Cai, Fufangchen Zhao +13
Video--based world models have emerged along two dominant paradigms: video generation and 3D reconstruction. However, existing evaluation benchmarks either focus narrowly on visual…
Latent Temporal Discrepancy as Motion Prior: A Loss-Weighting Strategy for Dynamic Fidelity in T2V
Meiqi Wu, Bingze Song, Ruimin Lin +5
Video generation models have achieved notable progress in static scenarios, yet their performance in motion video generation remains limited, with quality degrading under drastic d…
ImagerySearch: Adaptive Test-Time Search for Video Generation Beyond Semantic Dependency Constraints
Meiqi Wu, Jiashu Zhu, Xiaokun Feng +7
Video generation models have achieved remarkable progress, particularly excelling in realistic scenarios; however, their performance degrades notably in imaginative scenarios. Thes…
Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards
Honghao Chen, Xingzhou Lou, Xiaokun Feng +2
Chain of thought reasoning has demonstrated remarkable success in large language models, yet its adaptation to vision-language reasoning remains an open challenge with unclear best…
Finger in Camera Speaks Everything: Unconstrained Air-Writing for Real-World
Meiqi Wu, Kaiqi Huang, Yuanqiang Cai +3
Air-writing is a challenging task that combines the fields of computer vision and natural language processing, offering an intuitive and natural approach for human-computer interac…
How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language in Vision-Language Tracking
Xuchen Li, Shiyu Hu, Xiaokun Feng +4
Vision-language tracking (VLT) extends traditional single object tracking by incorporating textual information, providing semantic guidance to enhance tracking performance under ch…
DTVLT: A Multi-modal Diverse Text Benchmark for Visual Language Tracking Based on LLM
Xuchen Li, Shiyu Hu, Xiaokun Feng +4
Visual language tracking (VLT) has emerged as a cutting-edge research area, harnessing linguistic data to enhance algorithms with multi-modal inputs and broadening the scope of tra…
Visual Language Tracking with Multi-modal Interaction: A Robust Benchmark
Xuchen Li, Shiyu Hu, Xiaokun Feng +4
Visual Language Tracking (VLT) enhances tracking by mitigating the limitations of relying solely on the visual modality, utilizing high-level semantic information through language.…
Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness
Honghao Chen, Yurong Zhang, Xiaokun Feng +2
Robustness is a vital aspect to consider when deploying deep learning models into the wild. Numerous studies have been dedicated to the study of the robustness of vision transforme…
DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM
Xuchen Li, Xiaokun Feng, Shiyu Hu +4
Visual Language Tracking (VLT) enhances single object tracking (SOT) by integrating natural language descriptions from a video, for the precise tracking of a specified object. By l…
PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution
Honghao Chen, Xiangxiang Chu, Yongjian Ren +2
Recently, some large kernel convnets strike back with appealing performance and efficiency. However, given the square complexity of convolution, scaling up kernels can bring about…
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers
Shiyue Cao, Yueqin Yin, Lianghua Huang +4
Vector-quantized image modeling has shown great potential in synthesizing high-quality images. However, generating high-resolution images remains a challenging task due to the quad…