32 citations · 128 across the 27 of their papers we have counts for
30 papers
VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning
Zhenkun Gao, Yicheng Bao, Jinlong Peng +13
Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimo…
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection
Xu Lin, Jinlong Peng, Zhenye Gan +2
Existing Real-Time Object Detection (RTOD) methods commonly adopt YOLO-like architectures for their favorable trade-off between accuracy and speed. However, these models rely on st…
Soul: Breathe Life into Digital Human for High-fidelity Long-term Multimodal Animation
Jiangning Zhang, Junwei Zhu, Zhenye Gan +14
We propose a multimodal-driven framework for high-fidelity long-term digital human animation termed , which generates semantically coherent videos from a single-fram…
Transform Trained Transformer for Accelerating Native 4K Video Generation
Jiangning Zhang, Junwei Zhu, Teng Hu +9
Native 4K (21763840) video generation remains a critical challenge due to the quadratic computational explosion of full-attention as spatiotemporal resolution increases, ma…
RoleRMBench & RoleRM: Towards Reward Modeling for Profile-Based Role Play in Dialogue Systems
Hang Ding, Qiming Feng, Dongqi Liu +9
Reward modeling has become a cornerstone of aligning large language models (LLMs) with human preferences. Yet, when extended to subjective and open-ended domains such as role play,…
Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
Wenbing Zhu, Chengjie Wang, Bin-Bin Gao +12
Industrial Anomaly Detection (IAD) is a cornerstone for ensuring operational safety, maintaining product quality, and optimizing manufacturing efficiency. However, the advancement…
Human-MME: A Holistic Evaluation Benchmark for Human-Centric Multimodal Large Language Models
Yuansen Liu, Haiming Tang, Jinlong Peng +12
Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks. However, their capacity to comprehend human-centric scenes has rarely…
HumanVideo-MME: Benchmarking MLLMs for Human-Centric Video Understanding
Yuxuan Cai, Jiangning Zhang, Zhenye Gan +9
Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks involving both images and videos. However, their capacity to comprehen…
Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Qingdong He, Xueqin Chen, Chaoyi Wang +7
Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple and explicit instructions to ex…
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
Wenbing Zhu, Lidong Wang, Ziqing Zhou +14
The increasing complexity of industrial anomaly detection (IAD) has positioned multimodal detection methods as a focal area of machine vision research. However, dedicated multimoda…
UniCombine: Unified Multi-Conditional Combination with Diffusion Transformer
Haoxuan Wang, Jinlong Peng, Qingdong He +9
With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing. Although existing methods can g…
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation
Yanjie Pan, Qingdong He, Zhengkai Jiang +9
Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing ControlNet-like methods struggle…
SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
Chengjie Wang, Xi Jiang, Bin-Bin Gao +4
Although mainstream unsupervised anomaly detection (AD) (including image-level classification and pixel-level segmentation)algorithms perform well in academic datasets, their perfo…
MobileMamba: Lightweight Multi-Receptive Visual Mamba Network
Haoyang He, Jiangning Zhang, Yuxuan Cai +7
Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. CNNs, with their local receptive fields, struggle to capture long-range depende…
LLaVA-KD: A Framework of Distilling Multimodal Large Language Models
Yuxuan Cai, Jiangning Zhang, Haoyang He +7
The success of Large Language Models (LLMs) has inspired the development of Multimodal Large Language Models (MLLMs) for unified understanding of vision and language. However, the…
LLaVA-VSD: Large Language-and-Vision Assistant for Visual Spatial Description
Yizhang Jin, Jian Li, Jiangning Zhang +7
Visual Spatial Description (VSD) aims to generate texts that describe the spatial relationships between objects within images. Traditional visual spatial relationship classificatio…
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision
Chengjie Wang, Chengming Xu, Zhenye Gan +3
Positive and Unlabeled (PU) learning, a binary classification model trained with only positive and unlabeled data, generally suffers from overfitted risk estimation due to inconsis…
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection
Jiangning Zhang, Haoyang He, Zhenye Gan +7
Visual anomaly detection aims to identify anomalous regions in images through unsupervised learning paradigms, with increasing application demand and value in fields such as indust…
Efficient Multimodal Large Language Models: A Survey
Yizhang Jin, Jian Li, Yexin Liu +10
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.…
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Haoyang He, Yuhu Bai, Jiangning Zhang +7
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers ar…
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
Chengjie Wang, Wenbing Zhu, Bin-Bin Gao +6
Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid development. However, the recent development of IAD approach has encountered certain dif…
DMAD: Dual Memory Bank for Real-World Anomaly Detection
Jianlong Hu, Xu Chen, Zhenye Gan +7
Training a unified model is considered to be more suitable for practical industrial anomaly detection scenarios due to its generalization ability and storage efficiency. However, t…
A discovery of Two Slow Pulsars with FAST: "Ronin" from the Globular Cluster M15
Dengke Zhou, Pei Wang, Di Li +26
Globular clusters harbor numerous millisecond pulsars, but long-period pulsars ( ms) are rarely found. In this study, we employed a fast folding algorithm to analyze…
X-ray hardening preceding the onset of SGR 1935+2154's radio pulsar phase
Pei Wang, Jian Li, Long Ji +32
Magnetars are neutron stars with extremely strong magnetic fields, frequently powering high-energy activity in X-rays. Pulsed radio emission following some X-ray outbursts have bee…