10 citations · 27 across the 21 of their papers we have counts for
21 papers
HarnessWAM: Bridging Prediction and Deliberation in World Action Models
Zhaopeng Gu, Bingke Zhu, Tianxi Lin +9
World Action Models~(WAMs) jointly learn environmental dynamics and robot actions, introducing priors over physical evolution into embodied control. However, finite-horizon predict…
UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction
Zhaopeng Gu, Bingke Zhu, Zhaowen Li +5
Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few-shot transfer regime, the k…
Decoupled Similarity for Task-Aware Token Pruning in Large Vision-Language Models
Kexin Ma, Jing Xiao, Chaofeng Chen +4
Token pruning has emerged as an effective approach to reduce the substantial computational overhead of Large Vision-Language Models (LVLMs) by discarding less informative visual to…
Improving Generalization in LLM Structured Pruning via Function-Aware Neuron Grouping
Tao Yu, Yongqi An, Kuan Zhu +3
Large Language Models (LLMs) demonstrate impressive performance across natural language tasks but incur substantial computational and storage costs due to their scale. Post-trainin…
AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +4
Anomaly detection is a critical task across numerous domains and modalities, yet existing methods are often highly specialized, limiting their generalizability. These specialized m…
A Benchmark for Crime Surveillance Video Analysis with Large Models
Haoran Chen, Dong Yi, Moyan Cao +3
Anomaly analysis in surveillance videos is a crucial topic in computer vision. In recent years, multimodal large language models (MLLMs) have outperformed task-specific models in v…
MME-Industry: A Cross-Industry Multimodal Evaluation Benchmark
Dongyi Yi, Guibo Zhu, Chenglin Ding +3
With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across…
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +3
Anomaly detection methods typically require extensive normal samples from the target class for training, limiting their applicability in scenarios that require rapid adaptation, su…
UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +3
Visual Anomaly Detection (VAD) aims to identify abnormal samples in images that deviate from normal patterns, covering multiple domains, including industrial, logical, and medical…
Auto DragGAN: Editing the Generative Image Manifold in an Autoregressive Manner
Pengxiang Cai, Zhiwei Liu, Guibo Zhu +2
Pixel-level fine-grained image editing remains an open challenge. Previous works fail to achieve an ideal trade-off between control granularity and inference speed. They either fai…
Recurrent Context Compression: Efficiently Expanding the Context Window of LLM
Chensen Huang, Guibo Zhu, Xuepeng Wang +5
To extend the context length of Transformer-based large language models (LLMs) and improve comprehension capabilities, we often face limitations due to computational resources and…
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +4
Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories. Existing ap…
BFRFormer: Transformer-based generator for Real-World Blind Face Restoration
Guojing Ge, Qi Song, Guibo Zhu +5
Blind face restoration is a challenging task due to the unknown and complex degradation. Although face prior-based methods and reference-based methods have recently demonstrated hi…
ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model
Jianghao Chen, Pu Jian, Tengxiao Xi +7
During the development of large language models (LLMs), the scale and quality of the pre-training data play a crucial role in shaping LLMs' capabilities. To accelerate the research…
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +3
Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have demonstrated the capability of understanding images and achieved remarkable performance in various visual task…
FastBCSD: Fast and Efficient Neural Network for Binary Code Similarity Detection
Chensen Huang, Guibo Zhu, Guojing Ge +2
Binary code similarity detection (BCSD) has various applications, including but not limited to vulnerability detection, plagiarism detection, and malware detection. Previous resear…
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
Jian Zhao, Jianan Li, Lei Jin +20
The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UA…
Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition
Jinzhao Luo, Lu Zhou, Guibo Zhu +3
Skeleton-based action recognition has become popular in recent years due to its efficiency and robustness. Most current methods adopt graph convolutional network (GCN) for topology…
BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding
Yulong Liu, Yongqiang Ma, Wei Zhou +2
Due to the lack of paired samples and the low signal-to-noise ratio of functional MRI (fMRI) signals, reconstructing perceived natural images or decoding their semantic contents fr…
Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification
Tianyi Yan, Kuan Zhu, Haiyun guo +3
Clustering-based methods, which alternate between the generation of pseudo labels and the optimization of the feature extraction network, play a dominant role in both unsupervised…
Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation
Zhiwei Liu, Xiangyu Zhu, Lu Yang +7
3D human pose and shape recovery from a monocular RGB image is a challenging task. Existing learning based methods highly depend on weak supervision signals, e.g. 2D and 3D joint l…