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- Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k176
This report presents Open-Sora 2.0, a commercial-level video generation model trained for only $200k, and demonstrates that the cost of training a top-performing video generation model is highly controllable.
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- KeypointNet: A Large-Scale 3D Keypoint Dataset Aggregated From Numerous Human Annotations95
This work presents KeypointNet: the first large-scale and diverse 3D keypoint dataset that contains 83,231 keypoints and 8,329 3D models from 16 object categories, by leveraging numerous human annotations.
- Classification of ASD based on fMRI data with deep learning88
Experimental results indicate that the DFS can effectively select critical FC features for classification according to the weights of input FC features and the performance of GCN classifier can be improved dramatically.
- Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution75
A brand new point-set learning framework PRIN is proposed, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis, which shows that, on the dataset with randomly rotated point clouds, PRIN demonstrates better performance than state-of-the-art methods without any data augmentation.
- CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild54
This paper designs a novel Category-level PPF (CPPF) voting method to achieve accurate, robust and generalizable 9D pose estimation in the wild, and shows that the method is robust to noise and gives promising results under extremely challenging scenarios.
- Decoding human brain activity with deep learning54
A novel method of decoding brain activity evoked by visual stimuli by using an image-EEG-image transformation and shows that the proposed LSTM-CNN algorithm that discriminates the object classes by using EEG can be more accurate than the existing methods.
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- Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorization46
A novel approach that conditionally factorizes the reverse engineering of 3D CAD models from images into two sub-problems is introduced, and a TrAssembler that conditioned on the discrete structure with semantics predicts the continuous attribute values is proposed.
- Skeleton Merger: an Unsupervised Aligned Keypoint Detector45
It is demonstrated that Skeleton Merger is capable of detecting semantically-rich salient keypoints with good alignment, and shows comparable performance to supervised methods on the KeypointNet dataset, and it is also shown that the detector is robust to noise and subsampling.
- Classification of ADHD with fMRI data and multi-objective optimization34
The proposed multi-objective classification scheme addresses the imbalanced dataset problem by using a three objective SVM model with the positive and negative empirical errors being handled explicitly and separately, and effectively addresses dataset imbalanced problem from algorithm level.
- UKPGAN: A General Self-Supervised Keypoint Detector30
This work proposes UKPGAN, a general self-supervised 3D keypoint detector where keypoints are detected so that they could reconstruct the original object shape, and two modules: GAN-based keypoint sparsity control and salient information distillation modules are proposed to locate those important keypoints.
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- PRIN: Pointwise Rotation-Invariant Network.23
A new point sets learning framework named Pointwise Rotation-Invariant Network (PRIN), focusing on the rotation problem in point clouds, which shows similar performance on par or better than state-of-the-art methods on part segmentation without data augmentation.
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- Point cloud classification with deep normalized Reeb graph convolution19
This paper addresses the problem of classifying point clouds with unknown orientations by extracting the Reeb graph from point clouds, and then filter the graph with deep graph convolution, and shows that this method gives a competitive performance to the state-of-the-art methods and outperforms previous methods by a large margin.
- Combinatorial Q-Learning for Dou Di Zhu18
This paper studies a special class of Asian popular card games called Dou Di Zhu, in which two adversarial groups of agents must consider numerous card combinations at each time step, leading to huge number of actions, and proposes a novel method to handle combinatorial actions, which is called combinatorially Q-learning (CQL).
- Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes15
This work disentangle the direct offset into Local Canonical Coordinates (LCC), box scales and box orientations, which achieves state-of-the-art performance on three standard real-world benchmarks: ScanNet, SceneNN and SUN RGB-D.
- Robotic picking in dense clutter via domain invariant learning from synthetic dense cluttered rendering14
A novel domain-invariant Suction Quality Neural Network (diSQNN) is proposed to fuse realistic color feature and adversarial depth feature with a domain discriminator on depth extractor to reduce simulation-to-reality gap from synthetic images to low-quality RGB-D camera.
- CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation12
This paper introduces a novel method, CPPF++, designed for sim-to-real category-level pose estimation, which significantly surpasses previous sim-to-real approaches and achieves comparable or superior performance on novel datasets.
- PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments12
This work introduces PACE (Pose Annotations in Cluttered Environments), a large-scale benchmark designed to advance the development and evaluation of pose estimation methods in cluttered scenarios, and develops an innovative annotation system with a calibrated 3-camera setup.
- The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field12
The Snowflake Hypothesis is introduced -- a novel paradigm underpinning the concept of ``one node, one receptive field'' that can serve as a universal operator for a range of tasks, and it displays tremendous potential on deep GNNs.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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