activity
20192026
most citedTowards a Unified Evaluation of Explanation Methods without Ground Truth

7 citations · 11 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CV2026

Beyond a Single Frame: Multi-Frame Spatially Grounded Reasoning Across Volumetric MRI

Lama Moukheiber, Caleb M. Yeung, Haotian Xue +3

Spatial reasoning and visual grounding are core capabilities for vision-language models (VLMs), yet most medical VLMs produce predictions without transparent reasoning or spatial e…

cs.CV2026

Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation

Zeqian Long, Ozgur Kara, Haotian Xue +2

Image-to-video (I2V) generation has the potential for societal harm because it enables the unauthorized animation of static images to create realistic deepfakes. While existing def…

cs.CV2026

Laplacian Multi-scale Flow Matching for Generative Modeling

Zelin Zhao, Petr Molodyk, Haotian Xue +1

In this paper, we present Laplacian multiscale flow matching (LapFlow), a novel framework that enhances flow matching by leveraging multi-scale representations for image generative…

cs.CV2025

MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training

Haotian Xue, Qi Chen, Zhonghao Wang +4

Video diffusion models achieve strong frame-level fidelity but still struggle with motion coherence, dynamics and realism, often producing jitter, ghosting, or implausible dynamics…

cs.CV2025

Point-It-Out: Benchmarking Embodied Reasoning for Vision Language Models in Multi-Stage Visual Grounding

Haotian Xue, Yunhao Ge, Yu Zeng +4

Vision-Language Models (VLMs) have demonstrated impressive world knowledge across a wide range of tasks, making them promising candidates for embodied reasoning applications. Howev…

cs.CV2024

Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think

Haotian Xue, Yongxin Chen

Adversarial examples for diffusion models are widely used as solutions for safety concerns. By adding adversarial perturbations to personal images, attackers can not edit or imitat…