6 papers
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…
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…