24 citations · 52 across the 15 of their papers we have counts for
15 papers
InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention
Qiang Xiang, Shuang Sun, Binglei Li +6
Diffusion models have demonstrated remarkable capabilities in generating high-quality images. Recent advancements in Layout-to-Image (L2I) generation have leveraged positional cond…
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation
Jiahao Cheng, Tiancheng Su, Jia Yuan +5
Large Language Models (LLMs) often exhibit \textit{hallucinations}, generating factually incorrect or semantically irrelevant content in response to prompts. Chain-of-Thought (CoT)…
Dynamic Pyramid Network for Efficient Multimodal Large Language Model
Hao Ai, Kunyi Wang, Zezhou Wang +7
Multimodal large language models (MLLMs) have demonstrated impressive performance in various vision-language (VL) tasks, but their expensive computations still limit the real-world…
StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation
Zhengguang Zhou, Jing Li, Huaxia Li +2
Tuning-free personalized image generation methods have achieved significant success in maintaining facial consistency, i.e., identities, even with multiple characters. However, the…
Target-Driven Distillation: Consistency Distillation with Target Timestep Selection and Decoupled Guidance
Cunzheng Wang, Ziyuan Guo, Yuxuan Duan +4
Consistency distillation methods have demonstrated significant success in accelerating generative tasks of diffusion models. However, since previous consistency distillation method…
GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated Transformer
Jinpeng Yu, Binbin Huang, Yuxuan Zhang +3
Point cloud completion aims to recover accurate global geometry and preserve fine-grained local details from partial point clouds. Conventional methods typically predict unseen poi…
Unified Video-Language Pre-training with Synchronized Audio
Shentong Mo, Haofan Wang, Huaxia Li +1
Video-language pre-training is a typical and challenging problem that aims at learning visual and textual representations from large-scale data in a self-supervised way. Existing p…
StableGarment: Garment-Centric Generation via Stable Diffusion
Rui Wang, Hailong Guo, Jiaming Liu +6
In this paper, we introduce StableGarment, a unified framework to tackle garment-centric(GC) generation tasks, including GC text-to-image, controllable GC text-to-image, stylized G…
Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model
Yuxuan Zhang, Yirui Yuan, Yiren Song +1
Current makeup transfer methods are limited to simple makeup styles, making them difficult to apply in real-world scenarios. In this paper, we introduce Stable-Makeup, a novel diff…
InstantID: Zero-shot Identity-Preserving Generation in Seconds
Qixun Wang, Xu Bai, Haofan Wang +5
There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world applicability is hindered by…
SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation
Yuxuan Zhang, Yiren Song, Jiaming Liu +8
Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging. Addr…
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences
Shangkun Sun, Jiaming Liu, Thomas H. Li +3
Occlusions between consecutive frames have long posed a significant challenge in optical flow estimation. The inherent ambiguity introduced by occlusions directly violates the brig…
STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
Zhenglin Zhou, Huaxia Li, Hong Liu +3
Recently, deep learning-based facial landmark detection has achieved significant improvement. However, the semantic ambiguity problem degrades detection performance. Specifically,…
An Empirical Study of Propagation-based Methods for Video Object Segmentation
Hengkai Guo, Wenji Wang, Guanjun Guo +4
While propagation-based approaches have achieved state-of-the-art performance for video object segmentation, the literature lacks a fair comparison of different methods using the s…
Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis
Yueming Jin, Huaxia Li, Qi Dou +4
Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis and also very essential components in various a…