2 citations · 4 across the 16 of their papers we have counts for
19 papers
ORAV: Benchmarking Audio-Video Generation from Multimodal Contexts
Jiacheng Hua, Xiaokun Feng, Jiaqi Hua +3
Audio-video generation using heterogeneous multimodal references has emerged as a new challenge, requiring both compositional control over generation and grounded understanding of…
Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation
Chenxi Zhao, Chen Zhu, Xiaokun Feng +6
Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existing research on MeanFlow prima…
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…
HMVLA: Hyperbolic Multimodal Fusion for Vision-Language-Action Models
Kun Wang, Xiao Feng, Mingcheng Qu +1
Vision Language Action (VLA) models have recently shown great potential in bridging multimodal perception with robotic control. However, existing methods often rely on direct fine-…
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…
Stochastic Self-Guidance for Training-Free Enhancement of Diffusion Models
Chubin Chen, Jiashu Zhu, Xiaokun Feng +7
Classifier-free Guidance (CFG) is a widely used technique in modern diffusion models for enhancing sample quality and prompt adherence. However, through an empirical analysis on Ga…
Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation
Fangyuan Mao, Aiming Hao, Jintao Chen +7
Visual effects (VFX) are essential visual enhancements fundamental to modern cinematic production. Although video generation models offer cost-efficient solutions for VFX productio…
ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking
X. Feng, S. Hu, X. Li +5
Vision-language tracking aims to locate the target object in the video sequence using a template patch and a language description provided in the initial frame. To achieve robust t…
VMBench: A Benchmark for Perception-Aligned Video Motion Generation
Xinran Ling, Chen Zhu, Meiqi Wu +7
Video generation has advanced rapidly, improving evaluation methods, yet assessing video's motion remains a major challenge. Specifically, there are two key issues: 1) current moti…
Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues
X. Feng, D. Zhang, S. Hu +5
Vision-Language Tracking (VLT) aims to localize a target in video sequences using a visual template and language description. While textual cues enhance tracking potential, current…
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…
See Your Heart: Psychological states Interpretation through Visual Creations
Likun Yang, Xiaokun Feng, Xiaotang Chen +2
In psychoanalysis, generating interpretations to one's psychological state through visual creations is facing significant demands. The two main tasks of existing studies in the fie…
Split Semantic Detection in Sandplay Images
Xiaokun Feng, Xiaotang Chen, Jian Jia +1
Sandplay image, as an important psychoanalysis carrier, is a visual scene constructed by the client selecting and placing sand objects (e.g., sand, river, human figures, animals, v…