Search Results for author:
Found 16 papers, 7 papers with code
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning
In this paper, we propose a new approach that enables agents to learn whether their behaviors should be consistent with that of other agents by utilizing intrinsic rewards to learn the optimal policy for each agent.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Video Object of Interest Segmentation
Given a video and a target image of interest, our objective is to simultaneously segment and track all objects in the video that are relevant to the target image.
Weak-shot Semantic Segmentation via Dual Similarity Transfer
Proposal segmentation allows proposal-pixel similarity transfer from base classes to novel classes, which enables the mask learning of novel classes.
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL).
Learning Object Placement via Dual-path Graph Completion
Object placement aims to place a foreground object over a background image with a suitable location and size.
Finding Fallen Objects Via Asynchronous Audio-Visual Integration
The way an object looks and sounds provide complementary reflections of its physical properties.
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese.
Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary
As a result, we find that pixel-level annotation of base categories can facilitate affinity learning and propagation, leading to higher-quality CAMs of novel categories.
Inducing Reusable Skills From Demonstrations with Option-Controller Network
With the isolation of information and the synchronous calling mechanism, we can impose a division of works between the controller and options in an end-to-end training regime.
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
Experimental results suggest that 1) RL-based approaches struggle to solve most of the tasks efficiently; 2) gradient-based approaches, by optimizing open-loop control sequences with the built-in differentiable physics engine, can rapidly find a solution within tens of iterations, but still fall short on multi-stage tasks that require long-term planning.
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
To complete the task, an embodied agent must plan a sequence of actions to change the state of a large number of objects in the face of realistic physical constraints.
Learning Task Decomposition with Ordered Memory Policy Network
The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration.
Learning Task Decomposition with Order-Memory Policy Network
Many complex real-world tasks are composed of several levels of sub-tasks.
From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic Segmentation
Thus, we focus on zero-shot semantic segmentation, which aims to segment unseen objects with only category-level semantic representations provided for unseen categories.
Context-aware Feature Generation for Zero-shot Semantic Segmentation
In this paper, we propose a novel context-aware feature generation method for zero-shot segmentation named CaGNet.
Transferable Interactiveness Knowledge for Human-Object Interaction Detection
On account of the generalization of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results.
Ranked #29 on
Human-Object Interaction Detection
on V-COCO
