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Found 10 papers, 6 papers with code
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.
MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs
We benchmark over 20 state-of-the-art MLLMs and foundation models with potential multi-context visual grounding capabilities.
Continual Learning for Temporal-Sensitive Question Answering
In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA).
Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance
Moreover, to preserve the consistency information among multiple views, MIMB implements a biconsistency guidance strategy with reverse regularization of the consensus representation and proposes a manifold embedding measure for exploring the hidden structure of the recovered data.
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Text-based games provide an interactive way to study natural language processing.
H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection
How to align the source and target domains is critical to the CDWSOD accuracy.
Ranked #1 on
Weakly Supervised Object Detection
on Clipart1k
Goal Randomization for Playing Text-based Games without a Reward Function
In a variety of text-based games, we show that this simple method results in competitive performance for agents.
Generalization in Text-based Games via Hierarchical Reinforcement Learning
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+3
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language.
Self-Correction for Human Parsing
To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models.
Ranked #2 on
Human Part Segmentation
on PASCAL-Part
