15 citations · 54 across the 23 of their papers we have counts for
23 papers
What a Pooled Leaderboard Does Not Say: Auditing the Scope and Provenance of Method Comparisons in Microservice RCA Benchmarks
Lining Hu, Ting Liu, Yuzhuo Fu
Offline benchmarks for microservice root cause analysis (RCA) rank methods by a score pooled over several applications, and a pooled lead leaves two things unstated: on which of th…
VLRMBench: A Comprehensive and Challenging Benchmark for Vision-Language Reward Models
Jiacheng Ruan, Wenzhen Yuan, Xian Gao +6
Although large visual-language models (LVLMs) have demonstrated strong performance in multimodal tasks, errors may occasionally arise due to biases during the reasoning process. Re…
EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models
Zongyun Zhang, Jiacheng Ruan, Xian Gao +2
Industrial Anomaly Detection (IAD) is critical to ensure product quality during manufacturing. Although existing zero-shot defect segmentation and detection methods have shown effe…
GoAI: Enhancing AI Students' Learning Paths and Idea Generation via Graph of AI Ideas
Xian Gao, Zongyun Zhang, Ting Liu +1
With the rapid advancement of artificial intelligence technology, AI students are confronted with a significant "information-to-innovation" gap: they must navigate through the rapi…
ReviewAgents: Bridging the Gap Between Human and AI-Generated Paper Reviews
Xian Gao, Jiacheng Ruan, Zongyun Zhang +3
Academic paper review is a critical yet time-consuming task within the research community. With the increasing volume of academic publications, automating the review process has be…
From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes
Xian Gao, Jiacheng Ruan, Jingsheng Gao +4
Analyzing student behavior in educational scenarios is crucial for enhancing teaching quality and student engagement. Existing AI-based models often rely on classroom video footage…
Understanding Robustness of Parameter-Efficient Tuning for Image Classification
Jiacheng Ruan, Xian Gao, Suncheng Xiang +3
Parameter-efficient tuning (PET) techniques calibrate the model's predictions on downstream tasks by freezing the pre-trained models and introducing a small number of learnable par…
MM-CamObj: A Comprehensive Multimodal Dataset for Camouflaged Object Scenarios
Jiacheng Ruan, Wenzhen Yuan, Zehao Lin +5
Large visual-language models (LVLMs) have achieved great success in multiple applications. However, they still encounter challenges in complex scenes, especially those involving ca…
iDAT: inverse Distillation Adapter-Tuning
Jiacheng Ruan, Jingsheng Gao, Mingye Xie +4
Adapter-Tuning (AT) method involves freezing a pre-trained model and introducing trainable adapter modules to acquire downstream knowledge, thereby calibrating the model for better…
Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition
Zeyu Li, Suncheng Xiang, Tong Yu +5
The recognition of underwater audio plays a significant role in identifying a vessel while it is in motion. Underwater target recognition tasks have a wide range of applications in…
LAMM: Label Alignment for Multi-Modal Prompt Learning
Jingsheng Gao, Jiacheng Ruan, Suncheng Xiang +5
With the success of pre-trained visual-language (VL) models such as CLIP in visual representation tasks, transferring pre-trained models to downstream tasks has become a crucial pa…
GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction
Jiacheng Ruan, Jingsheng Gao, Mingye Xie +4
The Parameter-Efficient Fine-Tuning (PEFT) method, which adjusts or introduces fewer trainable parameters to calibrate pre-trained models on downstream tasks, has become a recent r…
EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation
Jiacheng Ruan, Mingye Xie, Jingsheng Gao +2
Transformer and its variants have been widely used for medical image segmentation. However, the large number of parameter and computational load of these models make them unsuitabl…
Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
Suncheng Xiang, Jingsheng Gao, Mengyuan Guan +5
Generalizable person re-identification (Re-ID) is a very hot research topic in machine learning and computer vision, which plays a significant role in realistic scenarios due to it…
CluCDD:Contrastive Dialogue Disentanglement via Clustering
Jingsheng Gao, Zeyu Li, Suncheng Xiang +2
A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines. Dialo…
Deep Multimodal Fusion for Generalizable Person Re-identification
Suncheng Xiang, Hao Chen, Wei Ran +4
Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervi…
MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
Jiacheng Ruan, Suncheng Xiang, Mingye Xie +2
Recently, some pioneering works have preferred applying more complex modules to improve segmentation performances. However, it is not friendly for actual clinical environments due…
MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation
Jiacheng Ruan, Mingye Xie, Suncheng Xiang +2
Recently, Visual Transformer (ViT) has been widely used in various fields of computer vision due to applying self-attention mechanism in the spatial domain to modeling global knowl…
Rethinking Person Re-Identification via Semantic-Based Pretraining
Suncheng Xiang, Jingsheng Gao, Zirui Zhang +5
Pretraining is a dominant paradigm in computer vision. Generally, supervised ImageNet pretraining is commonly used to initialize the backbones of person re-identification (Re-ID) m…
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification
Suncheng Xiang, Guanjie You, Mengyuan Guan +4
Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from…
Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification
Suncheng Xiang, Yuzhuo Fu, Mengyuan Guan +1
Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation. A potent…
Taking A Closer Look at Synthesis: Fine-grained Attribute Analysis for Person Re-Identification
Suncheng Xiang, Yuzhuo Fu, Guanjie You +1
Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from…
Attribute analysis with synthetic dataset for person re-identification
Suncheng Xiang, Yuzhuo Fu, Guanjie You +1
Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from…