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Works10 from public data
- A Survey of Embodied AI: From Simulators to Research Tasks597
An encyclopedic survey of the three main research tasks in embodied AI – visual exploration, visual navigation and embodied question answering – covering the state-of-the-art approaches, evaluation metrics and datasets is surveyed.
- Recognizing Emotion Cause in Conversations174
This work introduces a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provides a new highly challenging publicly available dialogue-level dataset for this task, and gives strong baseline results on this dataset.
- Aspect Sentiment Triplet Extraction Using Reinforcement Learning52
This work presents a novel paradigm, ASTE-RL, by regarding the aspect and opinion terms as arguments of the expressed sentiment in a hierarchical reinforcement learning (RL) framework that takes into account the mutual interactions among the triplet's components while improving exploration and sample efficiency.
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- SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments17
The SPACE simulator allows the SPACE dataset, a synthetic video dataset in a 3D environment, to be generated and evaluated with a state-of-the-art physics-based deep model and shows that the SPACE datasets improves the learning of intuitive physics with an approach inspired by curriculum learning.
- PIP: Physical Interaction Prediction via Mental Simulation with Span Selection13
The experiments show that PIP outperforms human, baseline, and related intuitive physics models that utilize mental simulation, and PIP's span selection module effectively identifies the frames indicating key physical interactions among objects, allowing for added interpretability.
- Single Image Video Prediction with Auto-Regressive GANs5
This paper introduces an approach for future frames prediction based on a single input image, able to generate an entire video sequence based on the information contained in the input frame, and adopts an autoregressive approach in the generation process.
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- JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning1
A theoretical motivation is provided showing that conventional JEPA objectives induce a predictive information bottleneck, and that conditional diffusion denoising admits a closely related predictive-compression decomposition, which motivate Joint Embedding DIffusion, the first online end-to-end latent diffusion world model.
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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 2026-10-12. 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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