1 citations · 1 across the 18 of their papers we have counts for
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OPIS: An Input-Grounded Benchmark for Multi-Object Memory in Video World Models
Hao Wang, Tao Yu, Liuzhou Zhang +13
Video world models must preserve the visual state of the world over time, but existing evaluation protocols often rely on generated histories, video reference, or selected revisit…
SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks
Tao Yu, Yifei Qu, Zhiqing Cui +14
Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA e…
When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
Tao Yu, Yujia Yang, Shenghua Chai +18
Video misinformation increasingly operates at the semantic and evidential level: authentic footage may be selectively edited, temporally reordered, spliced across sources, or augme…
HIMMEL: Hierarchical Interleaved Multi-stream Motion Encoding for Long Video Understanding
Haopeng Jin, Hongzhu Yi, Wenlong Zhao +9
Long video understanding with multimodal language models suffers from three compounding bottlenecks: heavy decode cost to obtain dense RGB frames, token growth with frame count, an…
Omni IIE Bench: Benchmarking the Practical Capabilities of Image Editing Models
Yujia Yang, Yuanxiang Wang, Zhenyu Guan +11
While Instruction-based Image Editing (IIE) has achieved significant progress, existing benchmarks pursue task breadth via mixed evaluations. This paradigm obscures a critical fail…
Towards Principled Dataset Distillation: A Spectral Distribution Perspective
Ruixi Wu, Shaobo Wang, Jiahuan Chen +9
Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic counterparts for efficient model training. However, existing DD methods exhibit substantial p…
ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search
Tao Yu, Haopeng Jin, Hao Wang +19
In recent years, large language models (LLMs) have made rapid progress in information retrieval, yet existing research has mainly focused on text or static multimodal settings. Ope…
Beyond Closed-Pool Video Retrieval: A Benchmark and Agent Framework for Real-World Video Search and Moment Localization
Tao Yu, Yujia Yang, Haopeng Jin +17
Traditional video retrieval benchmarks focus on matching precise descriptions to closed video pools, failing to reflect real-world searches characterized by fuzzy, multi-dimensiona…
VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents
Hongzhu Yi, Yujia Yang, Yuanxiang Wang +18
Image editing models can modify visual content through natural language, but editing dense documents requires precise local changes amid tightly coupled text, tables, and layout. W…
More Is Better: A MoE-Based Emotion Recognition Framework with Human Preference Alignment
Jun Xie, Yingjian Zhu, Feng Chen +9
In this paper, we present our solution for the semi-supervised learning track (MER-SEMI) in MER2025. We propose a comprehensive framework, grounded in the principle that "more is b…
Multimodal Video Emotion Recognition with Reliable Reasoning Priors
Zhepeng Wang, Yingjian Zhu, Guanghao Dong +4
This study investigates the integration of trustworthy prior reasoning knowledge from MLLMs into multimodal emotion recognition. We employ Gemini to generate fine-grained, modality…
Team of One: Cracking Complex Video QA with Model Synergy
Jun Xie, Zhaoran Zhao, Xiongjun Guan +5
We propose a novel framework for open-ended video question answering that enhances reasoning depth and robustness in complex real-world scenarios, as benchmarked on the CVRR-ES dat…
Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles
Jun Xie, Xiongjun Guan, Yingjian Zhu +5
In this paper, we present the runner-up solution for the Ego4D EgoSchema Challenge at CVPR 2025 (Confirmed on May 20, 2025). Inspired by the success of large models, we evaluate an…