21 citations · 48 across the 23 of their papers we have counts for
27 papers
UtilityBench: Measuring Language Model Utility When Errors Have Different Costs
Qifeng Lei, Zhiyong Yang, Zitai Wang +4
As language models are increasingly deployed in real-world scenarios with important outcomes, the impact of their mistakes often extends far beyond what correctness can capture. In…
Pairwise AUC Optimization Needs Corrective Power: A Unified View
Jia Chen, Zhiyong Yang, Shilong Bao +2
Direct pairwise AUC-surrogate training is attractive because it trains toward the ranking metric used at test time, especially under class imbalance. This creates a direct alignmen…
Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling
Cong Hua, Qianqian Xu, Zitai Wang +4
Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequencies or path confidence to co…
UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations
Peng Li, Qianqian Xu, Shilong Bao +2
Traffic video understanding has become an important problem in intelligent transportation, as road videos provide direct evidence for accidents, violations, and interactions betwee…
GoodDiffusion: Proactive Copyright Protection for Diffusion Bridge Models via Learnable Sample-specific Signatures
Shixi Qin, Zhiyong Yang, Shilong Bao +3
This paper tackles the challenging problem of developing a proactive copyright protection mechanism that cuts off unauthorized use of diffusion bridge models. Existing studies larg…
Understanding-Enhanced Model Collaboration for Long-Tailed Egocentric Mistake Detection
Boyu Han, Qianqian Xu, Shilong Bao +3
In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data. To this end, we propose an Understanding-Enhanced Mo…
Guiding Diffusion-based Reconstruction with Contrastive Signals for Balanced Visual Representation
Boyu Han, Qianqian Xu, Shilong Bao +4
The limited understanding capacity of the visual encoder in Contrastive Language-Image Pre-training (CLIP) has become a key bottleneck for downstream performance. This capacity inc…
BlackMirror: Black-Box Backdoor Detection for Text-to-Image Models via Instruction-Response Deviation
Feiran Li, Qianqian Xu, Shilong Bao +4
This paper investigates the challenging task of detecting backdoored text-to-image models under black-box settings and introduces a novel detection framework BlackMirror. Existing…
Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulations
Yangbangyan Jiang, Qianqian Xu, Huiyang Shao +4
As a variant of the Area Under the ROC Curve (AUC), the partial AUC (PAUC) focuses on a specific range of false positive rate (FPR) and/or true positive rate (TPR) in the ROC curve…
Towards Size-invariant Salient Object Detection: A Generic Evaluation and Optimization Approach
Shilong Bao, Qianqian Xu, Feiran Li +4
This paper investigates a fundamental yet underexplored issue in Salient Object Detection (SOD): the size-invariant property for evaluation protocols, particularly in scenarios whe…
Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection
Boyu Han, Qianqian Xu, Shilong Bao +3
In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data. To handle the challenges posed by subtle and infrequ…
LightFair: Towards an Efficient Alternative for Fair T2I Diffusion via Debiasing Pre-trained Text Encoders
Boyu Han, Qianqian Xu, Shilong Bao +3
This paper explores a novel lightweight approach LightFair to achieve fair text-to-image diffusion models (T2I DMs) by addressing the adverse effects of the text encoder. Most exis…
Hybrid Generative Fusion for Efficient and Privacy-Preserving Face Recognition Dataset Generation
Feiran Li, Qianqian Xu, Shilong Bao +3
In this paper, we present our approach to the DataCV ICCV Challenge, which centers on building a high-quality face dataset to train a face recognition model. The constructed datase…
One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing Framework
Feiran Li, Qianqian Xu, Shilong Bao +3
Concept erasing has recently emerged as an effective paradigm to prevent text-to-image diffusion models from generating visually undesirable or even harmful content. However, curre…
MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger Bridges
Shixi Qin, Zhiyong Yang, Shilong Bao +3
This paper focuses on implanting multiple heterogeneous backdoor triggers in bridge-based diffusion models designed for complex and arbitrary input distributions. Existing backdoor…
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning
Cong Hua, Qianqian Xu, Zhiyong Yang +3
Prompt tuning adapts Vision-Language Models like CLIP to open-world tasks with minimal training costs. In this direction, one typical paradigm evaluates model performance separatel…
Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification
Zhiguang Lu, Qianqian Xu, Shilong Bao +2
This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing…
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
Boyu Han, Qianqian Xu, Zhiyong Yang +4
The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have bee…
Improved Diversity-Promoting Collaborative Metric Learning for Recommendation
Shilong Bao, Qianqian Xu, Zhiyong Yang +3
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Fo…
Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection
Feiran Li, Qianqian Xu, Shilong Bao +4
This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We ob…
ReconBoost: Boosting Can Achieve Modality Reconcilement
Cong Hua, Qianqian Xu, Shilong Bao +2
This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal in…
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
Huiyang Shao, Qianqian Xu, Zhiyong Yang +2
The Partial Area Under the ROC Curve (PAUC), typically including One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average performance of a binary classifie…
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Shilong Bao, Qianqian Xu, Zhiyong Yang +3
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Fo…
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Wenzheng Hou, Qianqian Xu, Zhiyong Yang +3
It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a ty…