2 citations · 2 across the 15 of their papers we have counts for
8 papers · 1 filter
Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality
Zekai Luo, Zongze Du, Zhouhang Zhu +7
Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a signific…
Unified Open-World Segmentation with Multi-Modal Prompts
Yang Liu, Yufei Yin, Chenchen Jing +7
In this work, we present COSINE, a unified open-world segmentation model that consolidates open-vocabulary segmentation and in-context segmentation with multi-modal prompts (e.g.,…
Evolutionary Profiles for Protein Fitness Prediction
Jigang Fan, Xiaoran Jiao, Shengdong Lin +5
Predicting the fitness impact of mutations is central to protein engineering but constrained by limited assays relative to the size of sequence space. Protein language models (pLMs…
Learning by Imagining: Debiased Feature Augmentation for Compositional Zero-Shot Learning
Haozhe Zhang, Chenchen Jing, Mingyu Liu +2
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions by learning prior knowledge of seen primitives, \textit{i.e.}, attributes and objects…
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Shilong Li, Xingyuan Bu, Wenjie Wang +22
AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However, existing benchmarks such as BrowseCom…
Time Is a Feature: Exploiting Temporal Dynamics in Diffusion Language Models
Wen Wang, Bozhen Fang, Chenchen Jing +6
Diffusion large language models (dLLMs) generate text through iterative denoising, yet current decoding strategies discard rich intermediate predictions in favor of the final outpu…
PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training
Cong Chen, Mingyu Liu, Chenchen Jing +5
This paper aims to address the challenge of hallucinations in Multimodal Large Language Models (MLLMs) particularly for dense image captioning tasks. To tackle the challenge, we id…
Learning Visual Proxy for Compositional Zero-Shot Learning
Shiyu Zhang, Cheng Yan, Yang Liu +3
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions by leveraging knowledge from seen compositions. Current methods align textual prototyp…