726 citations · 4.8k across the 57 of their papers we have counts for
57 papers
StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation
Yupeng Zhou, Daquan Zhou, Ming-Ming Cheng +2
For recent diffusion-based generative models, maintaining consistent content across a series of generated images, especially those containing subjects and complex details, presents…
Sora Generates Videos with Stunning Geometrical Consistency
Xuanyi Li, Daquan Zhou, Chenxu Zhang +3
The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena…
A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation
Yunheng Li, Zhongyu Li, Shanghua Gao +3
Effectively modeling discriminative spatio-temporal information is essential for segmenting activities in long action sequences. However, we observe that existing methods are limit…
TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshes
Xuying Zhang, Bo-Wen Yin, Yuming Chen +4
Recent progress in the text-driven 3D stylization of a single object has been considerably promoted by CLIP-based methods. However, the stylization of multi-object 3D scenes is sti…
ChatAnything: Facetime Chat with LLM-Enhanced Personas
Yilin Zhao, Xinbin Yuan, Shanghua Gao +4
In this technical report, we target generating anthropomorphized personas for LLM-based characters in an online manner, including visual appearance, personality and tones, with onl…
Zone Evaluation: Revealing Spatial Bias in Object Detection
Zhaohui Zheng, Yuming Chen, Qibin Hou +3
A fundamental limitation of object detectors is that they suffer from "spatial bias", and in particular perform less satisfactorily when detecting objects near image borders. For a…
MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask
Yupeng Zhou, Daquan Zhou, Zuo-Liang Zhu +3
Recent advancements in diffusion models have showcased their impressive capacity to generate visually striking images. Nevertheless, ensuring a close match between the generated im…
DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
Bowen Yin, Xuying Zhang, Zhongyu Li +3
We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) Unlike previous…
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection
Yuming Chen, Xinbin Yuan, Jiabao Wang +4
We aim at providing the object detection community with an efficient and performant object detector, termed YOLO-MS. The core design is based on a series of investigations on how m…
CrossKD: Cross-Head Knowledge Distillation for Object Detection
Jiabao Wang, Yuming Chen, Zhaohui Zheng +3
Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object de…
Referring Camouflaged Object Detection
Xuying Zhang, Bowen Yin, Zheng Lin +3
We consider the problem of referring camouflaged object detection (Ref-COD), a new task that aims to segment specified camouflaged objects based on a small set of referring images…
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
Boyuan Sun, Yuqi Yang, Le Zhang +2
This paper presents a simple but performant semi-supervised semantic segmentation approach, called CorrMatch. Previous approaches mostly employ complicated training strategies to l…
Delving Deeper into Data Scaling in Masked Image Modeling
Cheng-Ze Lu, Xiaojie Jin, Qibin Hou +3
Understanding whether self-supervised learning methods can scale with unlimited data is crucial for training large-scale models. In this work, we conduct an empirical study on the…
AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
Zhen Li, Zuo-Liang Zhu, Ling-Hao Han +3
We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional cor…
Large Selective Kernel Network for Remote Sensing Object Detection
Yuxuan Li, Qibin Hou, Zhaohui Zheng +3
Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge pres…
StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing
Senmao Li, Joost van de Weijer, Taihang Hu +5
A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.They either finetune the model, or invert the…
SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution
Yupeng Zhou, Zhen Li, Chun-Le Guo +3
Previous works have shown that increasing the window size for Transformer-based image super-resolution models (e.g., SwinIR) can significantly improve the model performance. Still,…
Traffic Scene Parsing through the TSP6K Dataset
Peng-Tao Jiang, Yuqi Yang, Yang Cao +3
Traffic scene perception in computer vision is a critically important task to achieve intelligent cities. To date, most existing datasets focus on autonomous driving scenes. We obs…
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition
Cheng-Ze Lu, Xiaojie Jin, Zhicheng Huang +3
Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework, has shown its potential of learning expressive feature representations in visual image recognition. This…
Towards Spatial Equilibrium Object Detection
Zhaohui Zheng, Yuming Chen, Qibin Hou +2
Semantic objects are unevenly distributed over images. In this paper, we study the spatial disequilibrium problem of modern object detectors and propose to quantify this ``spatial…
Deep Negative Correlation Classification
Le Zhang, Qibin Hou, Yun Liu +4
Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm. Existing deep ensemble methods usually naively train many dif…
CamoFormer: Masked Separable Attention for Camouflaged Object Detection
Bowen Yin, Xuying Zhang, Qibin Hou +3
How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable…
Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition
Qibin Hou, Cheng-Ze Lu, Ming-Ming Cheng +1
This paper does not attempt to design a state-of-the-art method for visual recognition but investigates a more efficient way to make use of convolutions to encode spatial features.…
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou +3
We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to…