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20232026
most citedChatGPT-Powered Hierarchical Comparisons for Image Classification

11 citations · 19 across the 12 of their papers we have counts for

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cs.CV2026

SapiensID 2.0: Aligning Human Recognition Foundation Models with Human Perception

Yiyang Su, Jie Zhu, Feng Liu +2

While foundation models have significantly advanced human recognition across diverse modalities, they predominantly rely on static, geometric feature extraction. This approach fund…

cs.CV2026

FusionAgent: A Multimodal Agent with Dynamic Model Selection for Human Recognition

Jie Zhu, Xiao Guo, Yiyang Su +2

Model fusion is a key strategy for robust recognition in unconstrained scenarios, as different models provide complementary strengths. This is especially important for whole-body h…

cs.CV2026

Interpretable Perception and Reasoning for Audiovisual Geolocation

Yiyang Su, Xiaoming Liu

While recent advances in Multimodal Large Language Models (MLLMs) have improved image-based localization, precise global geolocation remains a formidable challenge due to the inher…

cs.CV2026

LocalScore: Local Density-Aware Similarity Scoring for Biometrics

Yiyang Su, Minchul Kim, Jie Zhu +4

Open-set biometrics faces challenges with probe subjects who may not be enrolled in the gallery, as traditional biometric systems struggle to detect these non-mated probes. Despite…

cs.CV2026

Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification?

Jie Zhu, Yiyang Su, Xiaoming Liu

Multi-modal large language models (MLLMs) exhibit strong general-purpose capabilities, yet still struggle on Fine-Grained Visual Classification (FGVC), a core perception task that…

cs.CV2025

HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID

Yiyang Su, Yunping Shi, Feng Liu +1

Recently, research interest in person re-identification (ReID) has increasingly focused on video-based scenarios, which are essential for robust surveillance and security in varied…

cs.CV2025

A Quality-Guided Mixture of Score-Fusion Experts Framework for Human Recognition

Jie Zhu, Yiyang Su, Minchul Kim +2

Whole-body biometric recognition is a challenging multimodal task that integrates various biometric modalities, including face, gait, and body. This integration is essential for ov…

cs.CV2025

Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait

Feng Liu, Nicholas Chimitt, Lanqing Guo +16

We address the problem of whole-body person recognition in unconstrained environments. This problem arises in surveillance scenarios such as those in the IARPA Biometric Recognitio…

cs.CV2025

SapiensID: Foundation for Human Recognition

Minchul Kim, Dingqiang Ye, Yiyang Su +2

Existing human recognition systems often rely on separate, specialized models for face and body analysis, limiting their effectiveness in real-world scenarios where pose, visibilit…

cs.CV2024★ 6 cited

Open-Set Biometrics: Beyond Good Closed-Set Models

Yiyang Su, Minchul Kim, Feng Liu +2

Biometric recognition has primarily addressed closed-set identification, assuming all probe subjects are in the gallery. However, most practical applications involve open-set biome…

cs.CV2024★ 2 cited

KeyPoint Relative Position Encoding for Face Recognition

Minchul Kim, Yiyang Su, Feng Liu +2

In this paper, we address the challenge of making ViT models more robust to unseen affine transformations. Such robustness becomes useful in various recognition tasks such as face…

cs.CV2023★ 11 cited

ChatGPT-Powered Hierarchical Comparisons for Image Classification

Zhiyuan Ren, Yiyang Su, Xiaoming Liu

The zero-shot open-vocabulary challenge in image classification is tackled by pretrained vision-language models like CLIP, which benefit from incorporating class-specific knowledge…

cs.CV2023

Separating Invisible Sounds Toward Universal Audiovisual Scene-Aware Sound Separation

Yiyang Su, Ali Vosoughi, Shijian Deng +2

The audio-visual sound separation field assumes visible sources in videos, but this excludes invisible sounds beyond the camera's view. Current methods struggle with such sounds la…