EventVL: Understand Event Streams via Multimodal Large Language Model

arXiv:2501.13707 · cs.CV, cs.AI · Submitted 2025-01-23 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-01-23

Updated: 2026-09-13

Comments: TMM 2026

Code: https://github.com/microsoft/DeepSpeed

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 41× in this paper · explained in 1 episode
• Semantic Alignment — 9× in this paper · explained in 3 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Gaussian Splatting — 5× in this paper · explained in 4 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Multimodal Large Language Model — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Optical Flow — 3× in this paper · explained in 2 episodes
• Semantic Understanding — 3× in this paper · explained in 2 episodes
• Vision-Language Model — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
• EZSR: Event-based Zero-Shot Recognition
• EventCLIP: Adapting CLIP for Event-based Object Recognition
• GPT-4 Technical Report
• How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
• DeepSeek-VL: Towards Real-World Vision-Language Understanding
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding
• u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
• Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
• VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
• Hawk: Learning to Understand Open-World Video Anomalies
• Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
• EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images
• EaDeblur-GS: Event assisted 3D Deblur Reconstruction with Gaussian Splatting
• IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera
• ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
• InternLM2 Technical Report
• LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

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