1 citations · 2 across the 14 of their papers we have counts for
15 papers
PartiCam: Camera Controlled Video Generation with Reward Guidance
Amine Ouasfi, Runjia Li, Junlin Han +3
We present PartiCam, a training-free Particle filtering rooted method for improved Camera controlled video generation. Generating videos that follow a precisely specified camera tr…
Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models
Chang Ma, Junlin Han, Shuo Chen +3
Red-teaming Text-to-Image (T2I) models is essential for safe deployment, yet it remains particularly challenging against implicit adversarial prompts. Unlike explicit adversarial p…
EgoPlay: Event-Triggered Video Editing for Egocentric Streams
Jinjie Mai, Gordon Guocheng Qian, Willi Menapace +8
We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data buil…
Reliable and Responsible Foundation Models: A Comprehensive Survey
Xinyu Yang, Junlin Han, Rishi Bommasani +49
Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), a…
EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing
Runjia Li, Moayed Haji-Ali, Ashkan Mirzaei +9
We study instruction-guided editing of egocentric videos for interactive AR applications. While recent AI video editors perform well on third-person footage, egocentric views prese…
VMem: Consistent Interactive Video Scene Generation with Surfel-Indexed View Memory
Runjia Li, Philip Torr, Andrea Vedaldi +1
We propose a novel memory module for building video generators capable of interactively exploring environments. Previous approaches have achieved similar results either by out-pain…
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
Zhaochong An, Guolei Sun, Yun Liu +4
Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to new classes with few support samples while retaining base class segmentation. Existing GFS-PCS methods e…
No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages
Youssef Mohamed, Runjia Li, Ibrahim Said Ahmad +4
Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in English; ArtEmis introduced subjecti…
Semantic Score Distillation Sampling for Compositional Text-to-3D Generation
Ling Yang, Zixiang Zhang, Junlin Han +4
Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. Due to the scarcity of 3D data, state-of-the-art a…
Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation
Zhaochong An, Guolei Sun, Yun Liu +5
Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal annotated support samples. While existing FS-PCS methods have sho…
DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge Transfer
Runjia Li, Junlin Han, Luke Melas-Kyriazi +6
We present DreamBeast, a novel method based on score distillation sampling (SDS) for generating fantastical 3D animal assets composed of distinct parts. Existing SDS methods often…
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies
Zhongrui Gui, Shuyang Sun, Runjia Li +5
Continual segmentation has not yet tackled the challenge of improving open-vocabulary segmentation models with training data for accurate segmentation across large, continually exp…
CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor
Shuyang Sun, Runjia Li, Philip Torr +2
Existing open-vocabulary image segmentation methods require a fine-tuning step on mask labels and/or image-text datasets. Mask labels are labor-intensive, which limits the number o…
OxfordTVG-HIC: Can Machine Make Humorous Captions from Images?
Runjia Li, Shuyang Sun, Mohamed Elhoseiny +1
This paper presents OxfordTVG-HIC (Humorous Image Captions), a large-scale dataset for humour generation and understanding. Humour is an abstract, subjective, and context-dependent…
Label Alignment Regularization for Distribution Shift
Ehsan Imani, Guojun Zhang, Runjia Li +4
Recent work has highlighted the label alignment property (LAP) in supervised learning, where the vector of all labels in the dataset is mostly in the span of the top few singular v…