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Found 14 papers, 4 papers with code
Pose Transferrable Person Re-Identification
On the other hand, in addition to the conventional discriminator of GAN (i. e., to distinguish between REAL/FAKE samples), we propose a novel guider sub-network which encourages the generated sample (i. e., with novel pose) towards better satisfying the ReID loss (i. e., cross-entropy ReID loss, triplet ReID loss).
Structure Preserving Video Prediction
Despite recent emergence of adversarial based methods for video prediction, existing algorithms often produce unsatisfied results in image regions with rich structural information (i. e., object boundary) and detailed motion (i. e., articulated body movement).
Fine-Grained Video Captioning for Sports Narrative
First, to facilitate this novel research of fine-grained video caption, we collected a novel dataset called Fine-grained Sports Narrative dataset (FSN) that contains 2K sports videos with ground-truth narratives from YouTube. com.
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
Sequential patterns play an important role in building modern recommender systems.
Normal Assisted Stereo Depth Estimation
We couple the learning of a multi-view normal estimation module and a multi-view depth estimation module.
Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness
In contrast, we propose adaptive thin volumes (ATVs); in an ATV, the depth hypothesis of each plane is spatially varying, which adapts to the uncertainties of previous per-pixel depth predictions.
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Learning to Regrasp by Learning to Place
In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses.
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration.
Using Augmented Face Images to Improve Facial Recognition Tasks
We present a framework that uses GAN-augmented images to complement certain specific attributes, usually underrepresented, for machine learning model training.
LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation
We also show that the learned skills can be reused to accelerate learning in new tasks domains and transfer to a physical robot platform.
Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning
Furthermore, the intrinsic heterogeneity in human behavior can produce equally successful but disparate demonstrations, further exacerbating the challenge of discerning demonstration quality.
Neural Field Dynamics Model for Granular Object Piles Manipulation
We present a learning-based dynamics model for granular material manipulation.
NOD-TAMP: Multi-Step Manipulation Planning with Neural Object Descriptors
Developing intelligent robots for complex manipulation tasks in household and factory settings remains challenging due to long-horizon tasks, contact-rich manipulation, and the need to generalize across a wide variety of object shapes and scene layouts.
A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches
Task and Motion Planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic tasks.
