3 citations · 3 across the 6 of their papers we have counts for
7 papers
FairReL: Deepfake Detection using Fairness-Aware Representation Learning
Xiaoman Lu, Jiaqi Li, Shuntian Zheng +2
Although recent deepfake detectors achieve high overall accuracy, their errors remain unevenly distributed across demographic subgroups, with real faces from certain groups more of…
Physics-Bounded mmWave Sensing for Schedulable, Privacy-Preserving Human Pose Estimation
Shuntian Zheng, Hongyang He, Jiaqi Li +6
Millimeter-wave (mmWave) is a promising modality for human pose estimation (HPE) in mobile deployments with strong privacy requirements and limited resources, such as fall detectio…
Doppler Prompting for Stable mmWave-based Human Pose Estimation
Shuntian Zheng, Jiaqi Li, Xiaoman Lu +2
Millimeter-wave (mmWave) enables privacy-preserving, illumination-robust human pose estimation (HPE), with each mmWave frame represented as a range-angle-Doppler tensor, providing…
Why Learn What Physics Already Knows? Realizing Agile mmWave-based Human Pose Estimation via Physics-Guided Preprocessing
Shuntian Zheng, Jiaqi Li, Minzhe Ni +2
We revisit millimeter-wave (mmWave) human pose estimation (HPE) from a signal preprocessing perspective. A single mmWave frame provides structured dimensions that map directly to h…
Keeping the Evidence Chain: Semantic Evidence Allocation for Training-Free Token Pruning in Video Temporal Grounding
Jiaqi Li, Shuntian Zheng, Yixian Shen +4
Video Temporal Grounding (VTG) localizes the temporal boundaries of query-relevant moments in long, untrimmed videos, making video-language-model prohibitively expensive. While rec…
Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization
Jiaqi Li, Guangming Wang, Shuntian Zheng +4
Temporal Action Localization (TAL) requires identifying both the boundaries and categories of actions in untrimmed videos. While vision-language models (VLMs) offer rich semantics…
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
Tianyu Yu, Haoye Zhang, Qiming Li +13
Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models. This leaves the community without foundational…