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Found 11 papers, 4 papers with code
AVicuna: Audio-Visual LLM with Interleaver and Context-Boundary Alignment for Temporal Referential Dialogue
In everyday communication, humans frequently use speech and gestures to refer to specific areas or objects, a process known as Referential Dialogue (RD).
OSCaR: Object State Captioning and State Change Representation
To address these challenges, in this paper, we introduce the Object State Captioning and State Change Representation (OSCaR) dataset and benchmark.
Video Understanding with Large Language Models: A Survey
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly.
MISAR: A Multimodal Instructional System with Augmented Reality
Augmented reality (AR) requires the seamless integration of visual, auditory, and linguistic channels for optimized human-computer interaction.
Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function.
Performances of Symmetric Loss for Private Data from Exponential Mechanism
This study explores the robustness of learning by symmetric loss on private data.
Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learning
In this work, we leverage instructional videos to study humans' decision-making processes, focusing on learning a model to plan goal-directed actions in real-life videos.
rQdia: Regularizing Q-Value Distributions With Image Augmentation
rQdia (pronounced “Arcadia”) regularizes Q-value distributions with augmented images in pixel-based deep reinforcement learning.
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences
The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences.
Learning from Interventions using Hierarchical Policies for Safe Learning
The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer.
Navigation by Imitation in a Pedestrian-Rich Environment
Deep neural networks trained on demonstrations of human actions give robot the ability to perform self-driving on the road.
