11 citations · 13 across the 9 of their papers we have counts for
7 papers · 1 filter
Image Generators are Generalist Vision Learners
Valentin Gabeur, Shangbang Long, Songyou Peng +22
Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language under…
Efficiently Reconstructing Dynamic Scenes One D4RT at a Time
Chuhan Zhang, Guillaume Le Moing, Skanda Koppula +11
Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simpl…
CyberV: Cybernetics for Test-time Scaling in Video Understanding
Jiahao Meng, Shuyang Sun, Yue Tan +4
Current Multimodal Large Language Models (MLLMs) may struggle with understanding long or complex videos due to computational demands at test time, lack of robustness, and limited a…
Diffusion Models Need Visual Priors for Image Generation
Xiaoyu Yue, Zidong Wang, Zeyu Lu +5
Conventional class-guided diffusion models generally succeed in generating images with correct semantic content, but often struggle with texture details. This limitation stems from…
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…
SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model
Bin Cao, Jianhao Yuan, Yexin Liu +4
In the rapidly evolving area of image synthesis, a serious challenge is the presence of complex artifacts that compromise perceptual realism of synthetic images. To alleviate artif…