420 citations · 2.4k across the 126 of their papers we have counts for
128 papers
SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation
Pengfei Chen, Lingxi Xie, Xinyue Huo +5
The Segment Anything model (SAM) has shown a generalized ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges. Th…
GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly Enhanced Quality
Taoran Yi, Jiemin Fang, Zanwei Zhou +7
Recently, 3D Gaussian splatting (3D-GS) has achieved great success in reconstructing and rendering real-world scenes. To transfer the high rendering quality to generation tasks, a…
Text-Animator: Controllable Visual Text Video Generation
Lin Liu, Quande Liu, Shengju Qian +5
Video generation is a challenging yet pivotal task in various industries, such as gaming, e-commerce, and advertising. One significant unresolved aspect within T2V is the effective…
Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation
Zelin Peng, Zhengqin Xu, Zhilin Zeng +2
Open-vocabulary semantic segmentation seeks to label each pixel in an image with arbitrary text descriptions. Vision-language foundation models, especially CLIP, have recently emer…
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
Jiannan Ge, Lingxi Xie, Hongtao Xie +4
A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accur…
GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting
Chen Yang, Sikuang Li, Jiemin Fang +5
Reconstructing and rendering 3D objects from highly sparse views is of critical importance for promoting applications of 3D vision techniques and improving user experience. However…
Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models
Xin He, Longhui Wei, Lingxi Xie +1
Multimodal Large Language Models (MLLMs) are experiencing rapid growth, yielding a plethora of noteworthy contributions in recent months. The prevailing trend involves adopting dat…
Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views
Yabo Chen, Jiemin Fang, Yuyang Huang +7
Synthesizing multi-view 3D from one single image is a significant but challenging task. Zero-1-to-3 methods have achieved great success by lifting a 2D latent diffusion model to th…
Segment Any 3D Gaussians
Jiazhong Cen, Jiemin Fang, Chen Yang +4
This paper presents SAGA (Segment Any 3D GAussians), a highly efficient 3D promptable segmentation method based on 3D Gaussian Splatting (3D-GS). Given 2D visual prompts as input,…
One-bit Supervision for Image Classification: Problem, Solution, and Beyond
Hengtong Hu, Lingxi Xie, Xinyue Hue +2
This paper presents one-bit supervision, a novel setting of learning with fewer labels, for image classification. Instead of training model using the accurate label of each sample,…
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model
Zelin Peng, Zhengqin Xu, Zhilin Zeng +3
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data. In the compute…
GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions
Junjie Wang, Jiemin Fang, Xiaopeng Zhang +2
Recently, impressive results have been achieved in 3D scene editing with text instructions based on a 2D diffusion model. However, current diffusion models primarily generate image…
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Yuhui Xu, Lingxi Xie, Xiaotao Gu +6
Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden l…
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models
Taoran Yi, Jiemin Fang, Junjie Wang +6
In recent times, the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3…
4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
Guanjun Wu, Taoran Yi, Jiemin Fang +6
Representing and rendering dynamic scenes has been an important but challenging task. Especially, to accurately model complex motions, high efficiency is usually hard to guarantee.…
Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models
Lingxi Xie, Longhui Wei, Xiaopeng Zhang +4
The AI community has been pursuing algorithms known as artificial general intelligence (AGI) that apply to any kind of real-world problem. Recently, chat systems powered by large l…
Visual Tuning
Bruce X. B. Yu, Jianlong Chang, Haixin Wang +9
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visu…
Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism
Xin Chen, Hengheng Zhang, Xiaotao Gu +3
The Mixture of Experts (MoE) model becomes an important choice of large language models nowadays because of its scalability with sublinear computational complexity for training and…
Segment Anything in 3D with Radiance Fields
Jiazhong Cen, Jiemin Fang, Zanwei Zhou +5
The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results. This paper aims to generalize SAM to segment 3D obj…
Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation
Xinyue Huo, Lingxi Xie, Wengang Zhou +2
We study unsupervised domain adaptation (UDA) for semantic segmentation. Currently, a popular UDA framework lies in self-training which endows the model with two-fold abilities: (i…
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation
Zelin Peng, Guanchun Wang, Lingxi Xie +3
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification net…
Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast
Kaifeng Bi, Lingxi Xie, Hengheng Zhang +3
In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by dow…
Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration
Yunjie Tian, Lingxi Xie, Jihao Qiu +4
We propose integrally pre-trained transformer pyramid network (iTPN), towards jointly optimizing the network backbone and the neck, so that transfer gap between representation mode…
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models
Chengcheng Ma, Yang Liu, Jiankang Deng +3
Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing…