3 citations · 4 across the 8 of their papers we have counts for
8 papers · 1 filter
EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation
Ruozhen He, Meng Wei, Ziyan Yang +1
Multi-shot video generation extends single-shot generation to coherent visual narratives, yet maintaining consistent characters, objects, and locations across shots remains a chall…
Agentic Discovery with Active Hypothesis Exploration for Visual Recognition
Jaywon Koo, Jefferson Hernandez, Ruozhen He +3
We introduce HypoExplore, an agentic framework that formulates neural architecture discovery for visual recognition as a hypothesis-driven scientific inquiry. Given a human-specifi…
Beyond Referring Expressions: Scenario Comprehension Visual Grounding
Ruozhen He, Nisarg A. Shah, Qihua Dong +3
Existing visual grounding benchmarks primarily evaluate alignment between image regions and literal referring expressions, where models can often succeed by matching a prominent na…
NoiseShift: Resolution-Aware Noise Recalibration for Better Low-Resolution Image Generation
Ruozhen He, Moayed Haji-Ali, Ziyan Yang +1
Text-to-image diffusion models often degrade when sampled at resolutions outside the final training resolution set. Prior work has largely emphasized higher resolution generation,…
GViT: Representing Images as Gaussians for Visual Recognition
Jefferson Hernandez, Ruozhen He, Guha Balakrishnan +2
We introduce GVIT, a classification framework that abandons conventional pixel or patch grid input representations in favor of a compact set of learnable 2D Gaussians. Each image i…
Fairness and Bias Mitigation in Computer Vision: A Survey
Sepehr Dehdashtian, Ruozhen He, Yi Li +4
Computer vision systems have witnessed rapid progress over the past two decades due to multiple advances in the field. As these systems are increasingly being deployed in high-stak…
Learning from Synthetic Data for Visual Grounding
Ruozhen He, Ziyan Yang, Paola Cascante-Bonilla +2
This paper extensively investigates the effectiveness of synthetic training data to improve the capabilities of vision-and-language models for grounding textual descriptions to ima…
Improved Visual Grounding through Self-Consistent Explanations
Ruozhen He, Paola Cascante-Bonilla, Ziyan Yang +2
Vision-and-language models trained to match images with text can be combined with visual explanation methods to point to the locations of specific objects in an image. Our work sho…