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- Visual interpretability for deep learning: a survey949
It is believed that high model interpretability may help people break several bottlenecks of deep learning, e.g., learning from a few annotations, learning via human–computer communications at the semantic level, and semantically debugging network representations.
- Interpretable Convolutional Neural Networks855
A method to modify a traditional convolutional neural network into an interpretable CNN, in order to clarify knowledge representations in high conv-layers of the CNN, which can help people understand the logic inside a CNN.
- Cross-View Action Modeling, Learning, and Recognition659
A novel multiview spatio-temporal and-or graph (MST-AOG) representation for cross-view action recognition, which takes advantage of the 3D human skeleton data obtained from Kinect cameras to avoid annotating enormous multi-view video frames, but the recognition does not need 3D information and is based on 2D video input.
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- Learning Human-Object Interactions by Graph Parsing Neural Networks596
This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images and videos with the Graph Parsing Neural Network (GPNN), a framework that incorporates structural knowledge while being differentiable end-to-end.
- Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning571
This work constructs a new largescale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language, and proposes a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (InterGPS).
- Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning476
A novel approach is proposed, PromptPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example, which verifies its effectiveness in selecting in- context examples.
- An Embodied Generalist Agent in 3D World453
This work introduces LEO, an embodied multi-modal generalist agent that excels in perceiving, grounding, reasoning, planning, and acting in the 3D world and demonstrates its remarkable proficiency across a wide spectrum of tasks, including 3D captioning, question answering, embodied reasoning, navigation and manipulation.
- RAVEN: A Dataset for Relational and Analogical Visual REasoNing421
This work proposes a new dataset, built in the context of Raven's Progressive Matrices (RPM) and aimed at lifting machine intelligence by associating vision with structural, relational, and analogical reasoning in a hierarchical representation and establishes a semantic link between vision and reasoning by providing structure representation.
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- Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation392
This paper proposes a pose grammar to tackle the problem of 3D human pose estimation, which takes 2D pose as input and learns a generalized 2D-3D mapping function and enforces high-level constraints over human poses.
- Diffusion-based Generation, Optimization, and Planning in 3D Scenes358
The SceneDiffuser is introduced, a conditional generative model for 3D scene understanding that jointly formulates the scene-aware generation, physics-based optimization, and goal-oriented planning via a diffusion-based denoising process in a fully differentiable fashion.
- Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds276
Experimental results demonstrate that, compared with supervised learning methods, the learned self-supervised representation facilitates various models to attain comparable or even better performances while capable of generalizing pre-trained models to downstream tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation.
- What are Textons?271
A three-level generative image model for learning textons from texture images and the geometric, dynamic, and photometric structures of the texton representation are studied by further extending the generative model to account for motion and illumination variations.
- Interpreting CNN Knowledge via an Explanatory Graph255
This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN in an unsupervised manner, i.e., without a need of annotating object parts.
- Introduction to a Large-Scale General Purpose Ground Truth Database: Methodology, Annotation Tool and Benchmarks246
A large scale general purpose image database with human annotated ground truth consisting of more than 636,748 annotated images and video frames is presented.
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- Human-Centric Indoor Scene Synthesis Using Stochastic Grammar209
A human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with the perfect per-pixel ground truth is presented.
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- DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare203
A novel end-to-end framework for jointly estimating 3D human pose and body shape from a monocular RGB image and a large-scale synthetic dataset utilizing web-crawled Mocap sequences, 3D scans and animations is constructed.
- On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models195
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning and identifies a variety of ML learning outcomes that depend solely on the implementation of MCMC sampling.
- CERN: Confidence-Energy Recurrent Network for Group Activity Recognition193
This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities, using a two-level hierarchy of Long Short-Term Memory networks, forming a feed-forward deep architecture, which can be trained end-to-end.
- Synthesizing Diverse and Physically Stable Grasps With Arbitrary Hand Structures Using Differentiable Force Closure Estimator188
A fast and differentiable force closure estimator, capable of producing diverse and physically stable grasps with arbitrary hand structures, without any training data is formulated, which can test a force closure within milliseconds.
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- Joint inference of groups, events and human roles in aerial videos187
This paper addresses a new problem of parsing low-resolution aerial videos of large spatial areas, in terms of grouping, recognizing events and 3) assigning roles to people engaged in events, using a spatiotemporal AND-OR graph.
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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