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Found 7 papers, 4 papers with code
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
To address this, we introduce XRL-Bench, a unified standardized benchmark tailored for the evaluation and comparison of XRL methods, encompassing three main modules: standard RL environments, explainers based on state importance, and standard evaluators.
Video Referring Expression Comprehension via Transformer with Content-conditioned Query
Video Referring Expression Comprehension (REC) aims to localize a target object in videos based on the queried natural language.
Improve Retrieval-based Dialogue System via Syntax-Informed Attention
Since incorporating syntactic information like dependency structures into neural models can promote a better understanding of the sentences, such a method has been widely used in NLP tasks.
Exploration and Regularization of the Latent Action Space in Recommendation
To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.
Video Referring Expression Comprehension via Transformer with Content-aware Query
To this end, we introduce two new datasets (i. e., VID-Entity and VidSTG-Entity) by augmenting the VIDSentence and VidSTG datasets with the explicitly referred words in the whole sentence, respectively.
Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem
In this paper, we redesign the patch-based triplet loss in MDE to alleviate the ubiquitous edge-fattening issue.
Ranked #1 on
Unsupervised Monocular Depth Estimation
on Kitti Raw
Correspondence Matters for Video Referring Expression Comprehension
Extensive experiments demonstrate that our DCNet achieves state-of-the-art performance on both video and image REC benchmarks.
