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Claim this profileWorks432 from public data
- 1,512
- Learning Fine-Grained Image Similarity with Deep Ranking1,376
A deep ranking model that employs deep learning techniques to learn similarity metric directly from images has higher learning capability than models based on hand-crafted features and deep classification models.
- 885
- 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.
- Vision-Based Gesture Recognition: A Review668
A survey on recent vision-based gesture recognition approaches is given and methods of static hand posture and temporal gesture recognition are reviewed, along with some thoughts about future research directions.
- 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.
- 531
- Minimax Entropy Principle and Its Application to Texture Modeling522
The minimax entropy principle is applied to texture modeling, where a novel Markov random field model, called FRAME, is derived, and encouraging results are obtained in experiments on a variety of texture images.
- Robust 3D Action Recognition with Random Occupancy Patterns501
This work extracts semi-local features called random occupancy pattern ROP features, which employ a novel sampling scheme that effectively explores an extremely large sampling space and utilizes a sparse coding approach to robustly encode these features.
- Multi-Agent Tensor Fusion for Contextual Trajectory Prediction477
This work encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and thescene context.
- 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.
- 476
- Interpreting CNNs via Decision Trees356
The proposed decision tree is a decision tree, which clarifies the specific reason for each prediction made by the CNN at the semantic level, and organizes all potential decision modes in a coarse-to-fine manner to explain CNN predictions at different fine-grained levels.
- 283
- 263
- 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.
- 254
- Deeply Learned Compositional Models for Human Pose Estimation253
A novel framework, termed as Deeply Learned Compositional Model (DLCM), is introduced, which exploits deep neural networks to learn the compositionality of human bodies and proposes a novel bone-based part representation that not only compactly encodes orientations, scales and shapes of parts, but also avoids their potentially large state spaces.
- 240
- Uncertainty-Aware Score Distribution Learning for Action Quality Assessment202
An uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA) is proposed, which regards an action as an instance associated with a score distribution, which describes the probability of different evaluated scores.
- 200
- 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.
- 188
- Learning Active Basis Model for Object Detection and Recognition183
An active basis model, a shared sketch algorithm, and a computational architecture of sum-max maps for representing, learning, and recognizing deformable templates are proposed.
- 178
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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