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- 1,435
- DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion1,203
DenseFusion is a generic framework for estimating 6D pose of a set of known objects from RGB-D images that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated.
- AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains542
This article proposes AnyGrasp for grasp perception to enable robots to grasp as robustly as humans using a parallel gripper and develops a dense supervision strategy with real perception and analytic labels in the spatial–temporal domain.
- Virtual to Real Reinforcement Learning for Autonomous Driving356
A novel realistic translation network is proposed to make model trained in virtual environment be workable in real world, and is believed to be the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.
- RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot288
This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception with a dataset comprising over 110,000 contact-rich robot manipulation sequences across diverse skills, contexts, robots, and camera viewpoints, all collected in the real world.
- Towards a new generation of artificial intelligence in China285
This Perspective briefly introduces the New Generation Artificial Intelligence Development Plan of China (2015–2030) from the point of view of the authors, a group of AI experts from academia and industry who have been involved in various stages of the plan.
- EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI206
This work introduces EmbodiedScan, a multi-modal, ego-centric 3D perception dataset and benchmark for holistic 3D scene understanding, and a baseline framework named Embodied Perceptron, capable of processing an arbitrary number of multi-modal inputs and demonstrating remarkable 3D perception capabilities.
- Symmetry and Group in Attribute-Object Compositions163
This paper proposes a previously ignored principle of attribute-object transformation: Symmetry and builds a transformation framework inspired by group theory, i.e. SymNet, which can be utilized for the Compositional Zero-Shot Learning task and outperforms the state-of-the-art on widely-used benchmarks.
- Detailed 2D-3D Joint Representation for Human-Object Interaction158
A detailed 2D-3D joint representation learning method for human-Object Interaction detection and a new benchmark named Ambiguous-HOI consisting of hard ambiguous images are proposed to better evaluate the 2D ambiguity processing capacity of models.
- Asynchronous Interaction Aggregation for Action Detection139
The Asynchronous Interaction Aggregation network (AIA) is proposed that leverages different interactions to boost action detection and can gain notable accuracy from the integrative interactions and is easy to train end-to-end.
- ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis121
This work proposes ArtiBoost, a lightweight online data enhancement method that performs data exploration and synthesis within a learning pipeline, and those synthetic data are blended into real-world source data for training.
- FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation83
FoAR is proposed, a force-aware reactive policy that combines high-frequency force/torque sensing with visual inputs to enhance the performance in contact-rich manipulation.
- Upcycling Models Under Domain and Category Shift75
This paper designs a novel, adaptive one-vs-all global clustering algorithm to achieve the distinction across different target classes and introduces a local k-NN clustering strategy to alleviate negative transfer.
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- Real-time contrast preserving decolorization63
Decolorization methods are typically orders of magnitude slower than standard procedures such as matlab rgb2gray function, which largely hinders their practical use.
- Deep RNN Framework for Visual Sequential Applications56
This work proposes a new recurrent neural framework that can be stacked deep effectively and provides empirical evidence to show that the deep RNN framework is easy to optimize and can gain accuracy from the increased depth on several visual sequence problems.
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- Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition42
A joint learning framework for mutually assisted “interacted object localization” and “human action recognition” based on skeleton data is proposed and the temporal consistency of interacted object is explored as constraint to better localize the interacted object with the absence of ground-truth labels.
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- Approximated Bilinear Modules for Temporal Modeling33
It is shown how two-layer subnets in CNNs can be converted to temporal bilinear modules by adding an auxiliary-branch, and snippet sampling and shifting inference are introduced to boost sparse-frame video classification performance.
- Complex sequential understanding through the awareness of spatial and temporal concepts28
A new modelling strategy—‘semi-coupled structure’ (SCS)—which consists of deep neural networks that decouple the complex spatial and temporal concepts during learning is introduced, demonstrating that SCS has the capacity to improve the performance of long short-term memory (LSTM)-like models on large-scale sequential tasks.
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- Template-Instance Loss for Offline Handwritten Chinese Character Recognition26
A character template is designed to deal with the intrinsic similarities among Chinese characters and the instance loss can reduce category variance according to classification difficulty, giving a large penalty to the outlier instance of handwritten Chinese character.
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 2026-10-14. 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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