6 citations · 22 across the 20 of their papers we have counts for
21 papers
Hyper-RED: Scalable Event Pre-training via Semantic Hypergraph Distillation
Meisen Wang, Zhiqiang Tian, Wei Bao +3
Event cameras have shown great potential for robust visual perception, yet scaling event representation learning remains challenging due to the scarcity of large-scale annotated ev…
When Policy Entropy Constraint Fails: Preserving Diversity in Flow-based RLHF via Perceptual Entropy
Xiaofeng Tan, Jun Liu, Bin-Bin Gao +5
RLHF is widely used to align flow-matching text-to-image models with human preferences, but often leads to severe diversity collapse after fine-tuning. In RL, diversity is often as…
L2P: Unlocking Latent Potential for Pixel Generation
Zhennan Chen, Junwei Zhu, Xu Chen +7
Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data…
Rethinking Event-Based Object Detection through Representation-Level Temporal Aggregation and Model-Level Hypergraph Reasoning
Meisen Wang, Hao Deng, Wei Bao +5
Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, offering potential for perception under fast motion and challenging illumination c…
Large-Scale Universal Defect Generation: Foundation Models and Datasets
Yuanting Fan, Jun Liu, Bin-Bin Gao +5
Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scale paired defect editing data.…
PET-DINO: Unifying Visual Cues into Grounding DINO with Prompt-Enriched Training
Weifu Fu, Jinyang Li, Bin-Bin Gao +6
Open-Set Object Detection (OSOD) enables recognition of novel categories beyond fixed classes but faces challenges in aligning text representations with complex visual concepts and…
ConsistentRFT: Reducing Visual Hallucinations in Flow-based Reinforcement Fine-Tuning
Xiaofeng Tan, Jun Liu, Yuanting Fan +7
Reinforcement Fine-Tuning (RFT) on flow-based models is crucial for preference alignment. However, they often introduce visual hallucinations like over-optimized details and semant…
One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection
Bin-Bin Gao, Chengjie Wang
Universal visual anomaly detection (AD) aims to identify anomaly images and segment anomaly regions towards open and dynamic scenarios, following zero- and few-shot paradigms witho…
Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection
Yuanting Fan, Jun Liu, Xiaochen Chen +5
Few-shot anomaly detection (FSAD) methods identify anomalous regions with few known normal samples. Most existing methods rely on the generalization ability of pre-trained vision-l…
DRL: Discriminative Representation Learning with Parallel Adapters for Class Incremental Learning
Jiawei Zhan, Jun Liu, Jinlong Peng +4
With the excellent representation capabilities of Pre-Trained Models (PTMs), remarkable progress has been made in non-rehearsal Class-Incremental Learning (CIL) research. However,…
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang +6
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The ex…
Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation
Guan Gui, Bin-Bin Gao, Jun Liu +2
Anomaly detection is a practical and challenging task due to the scarcity of anomaly samples in industrial inspection. Some existing anomaly detection methods address this issue by…
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning
Jiawei Zhan, Jinxiang Lai, Bin-Bin Gao +3
Anomaly detection, the technique of identifying abnormal samples using only normal samples, has attracted widespread interest in industry. Existing one-model-per-category methods o…
MatchDet: A Collaborative Framework for Image Matching and Object Detection
Jinxiang Lai, Wenlong Wu, Bin-Bin Gao +5
Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i.e. task-individual). In this pa…
Clustered-patch Element Connection for Few-shot Learning
Jinxiang Lai, Siqian Yang, Junhong Zhou +5
Weak feature representation problem has influenced the performance of few-shot classification task for a long time. To alleviate this problem, recent researchers build connections…
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Wu +8
Recent Few-Shot Learning (FSL) methods put emphasis on generating a discriminative embedding features to precisely measure the similarity between support and query sets. Current CN…
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
Jiawei Zhan, Jun Liu, Wei Tang +8
Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Al…
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
Jinxiang Lai, Siqian Yang, Guannan Jiang +9
Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the…
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Liu +6
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples. M…
How to Reduce Change Detection to Semantic Segmentation
Guo-Hua Wang, Bin-Bin Gao, Chengjie Wang
Change detection (CD) aims to identify changes that occur in an image pair taken different times. Prior methods devise specific networks from scratch to predict change masks in pix…
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Most of advanced solutions exploit a metri…