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Found 24 papers, 12 papers with code
MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation
The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field.
Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method
However, since the off-the-shelf pre-trained networks are trained on clean images, the one-step estimation procedure of the clean image may be inaccurate, especially in the early stages of the generation process in diffusion models.
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process.
AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text
We study the problem of creating high-fidelity and animatable 3D avatars from only textual descriptions.
MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion.
MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation
The flexibility of these techniques enables the editing of arbitrary regions within the frame.
MagicEdit: High-Fidelity and Temporally Coherent Video Editing
In this report, we present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task.
MagicAvatar: Multimodal Avatar Generation and Animation
This report presents MagicAvatar, a framework for multimodal video generation and animation of human avatars.
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models (DPMs) with user-provided concepts.
DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing
In this work, we extend this editing framework to diffusion models and propose a novel approach DragDiffusion.
Delving Deeper into Data Scaling in Masked Image Modeling
The study reveals that: 1) MIM can be viewed as an effective method to improve the model capacity when the scale of the training data is relatively small; 2) Strong reconstruction targets can endow the models with increased capacities on downstream tasks; 3) MIM pre-training is data-agnostic under most scenarios, which means that the strategy of sampling pre-training data is non-critical.
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs.
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS).
Knowledge Distillation
Open Vocabulary Semantic Segmentation
+4
PV3D: A 3D Generative Model for Portrait Video Generation
While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos.
MagicMix: Semantic Mixing with Diffusion Models
Unlike style transfer, where an image is stylized according to the reference style without changing the image content, semantic blending mixes two different concepts in a semantic manner to synthesize a novel concept while preserving the spatial layout and geometry.
SODAR: Segmenting Objects by DynamicallyAggregating Neighboring Mask Representations
Unlike the original per grid cell object masks, SODAR is implicitly supervised to learn mask representations that encode geometric structure of nearby objects and complement adjacent representations with context.
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs
Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods.
Body Meshes as Points
In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.
Ranked #9 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Human Shape Estimation
3D Multi-Person Pose Estimation
+1
AggMask: Exploring locally aggregated learning of mask representations for instance segmentation
Recently proposed one-stage instance segmentation models (\emph{e. g.}, SOLO) learn to directly predict location-specific object mask with fully-convolutional networks.
Classification Calibration for Long-tail Instance Segmentation
In this report, we investigate the performance drop phenomenon of state-of-the-art two-stage instance segmentation models when processing extreme long-tail training data based on the LVIS [5] dataset, and find a major cause is the inaccurate classification of object proposals.
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input
Existing deep learning-based interactive image segmentation approaches typically assume the target-of-interest is always a single object and fail to account for the potential diversity in user expectations, thus requiring excessive user input when it comes to segmenting an object part or a group of objects instead.
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.
Ranked #70 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation
In multi-label brain tumor segmentation, class imbalance and inter-class interference are common and challenging problems.
Regional Interactive Image Segmentation Networks
The interactive image segmentation model allows users to iteratively add new inputs for refinement until a satisfactory result is finally obtained.
Ranked #10 on
Interactive Segmentation
on SBD
(NoC@85 metric)
