20 citations · 48 across the 32 of their papers we have counts for
17 papers · 1 filter
Tether the Subject, Release the Scene: Query-Aware Memory Routing for Long-Horizon Autoregressive Video Generation
Chen Li, Peng Zhang, Hanyu Zhou +5
Streaming autoregressive video models generate long videos chunk by chunk, using historical memory to maintain consistency. Existing methods typically expose subject and scene quer…
Achieving Text-based Person Retrieval with Any Granularity
Jialong Zuo, Hanyu Zhou, Dongyue Wu +5
Text-based person retrieval faces a critical but under-explored challenge: the inherent uncertainty of query granularity in real-world scenarios. This paper introduces a new paradi…
FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling
Jialong Zuo, Haotong Zuo, Shiwei Zhang +5
Translating novels into films poses a grand challenge for generative artificial intelligence, requiring conversion of abstract literary prose into long-form, multi-scene visual nar…
INTACT: Ego-Guided Typed Sparse Evidence Retrieval for Heterogeneous Collaborative Perception
Chen Li, Shengrong Yuan, Jialong Zuo +3
Collaborative perception extends the perceptual range of autonomous vehicles by sharing information across agents, but heterogeneous sensors and perception models make intermediate…
Is Your Driving World Model an All-Around Player?
Lingdong Kong, Ao Liang, Tianyi Yan +20
Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic phys…
Is Nano Banana Pro a Low-Level Vision All-Rounder? A Comprehensive Evaluation on 14 Tasks and 40 Datasets
Jialong Zuo, Haoyou Deng, Hanyu Zhou +10
The rapid evolution of text-to-image generation models has revolutionized visual content creation. While commercial products like Nano Banana Pro have garnered significant attentio…
WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
Ao Liang, Lingdong Kong, Tianyi Yan +19
Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. D…
Learning to Tell Apart: Weakly Supervised Video Anomaly Detection via Disentangled Semantic Alignment
Wenti Yin, Huaxin Zhang, Xiang Wang +7
Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal founda…
VideoLucy: Deep Memory Backtracking for Long Video Understanding
Jialong Zuo, Yongtai Deng, Lingdong Kong +7
Recent studies have shown that agent-based systems leveraging large language models (LLMs) for key information retrieval and integration have emerged as a promising approach for lo…
Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration
Dongyue Wu, Zilin Guo, Jialong Zuo +2
The ever-growing size of training datasets enhances the generalization capability of modern machine learning models but also incurs exorbitant computational costs. Existing data pr…
ReID5o: Achieving Omni Multi-modal Person Re-identification in a Single Model
Jialong Zuo, Yongtai Deng, Mengdan Tan +5
In real-word scenarios, person re-identification (ReID) expects to identify a person-of-interest via the descriptive query, regardless of whether the query is a single modality or…
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity
Huaxin Zhang, Xiaohao Xu, Xiang Wang +6
How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts? Traditional Video Anomaly Understanding (VAU) methods focus on frame-lev…
Cross-video Identity Correlating for Person Re-identification Pre-training
Jialong Zuo, Ying Nie, Hanyu Zhou +5
Recent researches have proven that pre-training on large-scale person images extracted from internet videos is an effective way in learning better representations for person re-ide…
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM
Huaxin Zhang, Xiaohao Xu, Xiang Wang +6
Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address…
Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification
Jiahao Hong, Jialong Zuo, Chuchu Han +4
Recent unsupervised person re-identification (re-ID) methods achieve high performance by leveraging fine-grained local context. These methods are referred to as part-based methods.…
UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity
Jialong Zuo, Hanyu Zhou, Ying Nie +5
Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts…
PLIP: Language-Image Pre-training for Person Representation Learning
Jialong Zuo, Jiahao Hong, Feng Zhang +5
Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, th…