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Found 25 papers, 13 papers with code
Move Anything with Layered Scene Diffusion
Diffusion models generate images with an unprecedented level of quality, but how can we freely rearrange image layouts?
InsActor: Instruction-driven Physics-based Characters
Generating animation of physics-based characters with intuitive control has long been a desirable task with numerous applications.
DreamGaussian4D: Generative 4D Gaussian Splatting
Remarkable progress has been made in 4D content generation recently.
FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing
Notably, FineMoGen further enables zero-shot motion editing capabilities with the aid of modern large language models (LLM), which faithfully manipulates motion sequences with fine-grained instructions.
Ranked #2 on
Motion Synthesis
on KIT Motion-Language
GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation
In this study, we explore Transformer-based diffusion models for image and video generation.
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
In this paper, for the first time, we introduce optical flow into the attention module in the diffusion model's U-Net to address the inconsistency issue for text-to-video editing.
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks.
Underwater-Art: Expanding Information Perspectives With Text Templates For Underwater Acoustic Target Recognition
In our work, we propose to implement Underwater Acoustic Recognition based on Templates made up of rich relevant information (hereinafter called "UART").
Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform
Background noise and variable channel transmission environment make it complicated to implement accurate ship-radiated noise recognition.
RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions
Our experiments further demonstrate that pre-training and depth-free BEV transformation has the potential to enhance out-of-distribution robustness.
DiffMimic: Efficient Motion Mimicking with Differentiable Physics
Motion mimicking is a foundational task in physics-based character animation.
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications.
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases.
LaserMix for Semi-Supervised LiDAR Semantic Segmentation
Densely annotating LiDAR point clouds is costly, which restrains the scalability of fully-supervised learning methods.
Sparse Mixture-of-Experts are Domain Generalizable Learners
It is motivated by an empirical finding that transformer-based models trained with empirical risk minimization (ERM) outperform CNN-based models employing state-of-the-art (SOTA) DG algorithms on multiple DG datasets.
Ranked #11 on
Domain Generalization
on DomainNet
(using extra training data)
Balanced MSE for Imbalanced Visual Regression
Data imbalance exists ubiquitously in real-world visual regressions, e. g., age estimation and pose estimation, hurting the model's generalizability and fairness.
Benchmarking and Analyzing Point Cloud Classification under Corruptions
3D perception, especially point cloud classification, has achieved substantial progress.
Ranked #7 on
Point Cloud Classification
on PointCloud-C
Playing for 3D Human Recovery
Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios.
Bayesian Imbalanced Regression Debiasing
Compared to imbalanced and long-tailed classification, imbalanced regression has its unique challenges as the regression label space can be continuous, boundless, and high-dimensional.
HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification
Person Re-Identification (Re-ID) is of great importance to the many video surveillance systems.
REFINE: Prediction Fusion Network for Panoptic Segmentation
Panoptic segmentation aims at generating pixel-wise class and instance predictions for each pixel in the input image, which is a challenging task and far more complicated than naively fusing the semantic and instance segmentation results.
Ranked #11 on
Panoptic Segmentation
on COCO test-dev
Balanced Activation for Long-tailed Visual Recognition
Deep classifiers have achieved great success in visual recognition.
Leveraging Localization for Multi-camera Association
We present McAssoc, a deep learning approach to the as-sociation of detection bounding boxes in different views ofa multi-camera system.
Balanced Meta-Softmax for Long-Tailed Visual Recognition
In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks.
Ranked #7 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
In this paper, we present STAR, a Spatio-Temporal grAph tRansformer framework, which tackles trajectory prediction by only attention mechanisms.
