70 citations · 82 across the 10 of their papers we have counts for
11 papers
Technical Report on the CVPR 2026@AdvML Workshop Challenge
Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47
Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Cha…
Multi-view Gaze Target Estimation
Qiaomu Miao, Vivek Raju Golani, Jingyi Xu +3
This paper presents a method that utilizes multiple camera views for the gaze target estimation (GTE) task. The approach integrates information from different camera views to impro…
Importance-Based Token Merging for Efficient Image and Video Generation
Haoyu Wu, Jingyi Xu, Hieu Le +1
Token merging can effectively accelerate various vision systems by processing groups of similar tokens only once and sharing the results across them. However, existing token groupi…
Learning Frame-Wise Emotion Intensity for Audio-Driven Talking-Head Generation
Jingyi Xu, Hieu Le, Zhixin Shu +3
Human emotional expression is inherently dynamic, complex, and fluid, characterized by smooth transitions in intensity throughout verbal communication. However, the modeling of suc…
Assessing Sample Quality via the Latent Space of Generative Models
Jingyi Xu, Hieu Le, Dimitris Samaras
Advances in generative models increase the need for sample quality assessment. To do so, previous methods rely on a pre-trained feature extractor to embed the generated samples and…
Zero-Shot Object Counting with Language-Vision Models
Jingyi Xu, Hieu Le, Dimitris Samaras
Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods re…
Zero-shot Object Counting
Jingyi Xu, Hieu Le, Vu Nguyen +2
Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods re…
Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices
Jingyi Xu, Tushar Vaidya, Yufei Wu +3
We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult proce…
Generating Representative Samples for Few-Shot Classification
Jingyi Xu, Hieu Le
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot class representations are often biased due to data scarcity. To mitigate this iss…
Learning Clusterable Visual Features for Zero-Shot Recognition
Jingyi Xu, Zhixin Shu, Dimitris Samaras
In zero-shot learning (ZSL), conditional generators have been widely used to generate additional training features. These features can then be used to train the classifiers for tes…
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman +2
This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output…