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Found 6 papers, 1 papers with code
R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning
In this work, we present the first application of Reinforcement Learning with Verifiable Reward (RLVR) to an Omni-multimodal large language model in the context of emotion recognition, a task where both visual and audio modalities play crucial roles.
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding
In human-centric scenes, the ability to simultaneously understand visual and auditory information is crucial.
Facial Dynamics in Video: Instruction Tuning for Improved Facial Expression Perception and Contextual Awareness
Facial expression captioning has found widespread application across various domains.
LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding
In this paper, we introduce LLaVA-Octopus, a novel video multimodal large language model.
D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes
To address this issue, we propose D-Aug, a LiDAR data augmentation method tailored for augmenting dynamic scenes.
GLIPv3
In the open-set object detection, the alignment of visual and text features is one of the most important factors affecting the final detection performance.
