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Found 16 papers, 6 papers with code
GenieBlue: Integrating both Linguistic and Multimodal Capabilities for Large Language Models on Mobile Devices
Recent advancements in Multimodal Large Language Models (MLLMs) have enabled their deployment on mobile devices.
SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant?
Large Language Models (LLMs) have become integral to daily life, especially advancing as intelligent assistants through on-device deployment on smartphones.
Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
Existing video tokenizers are based on the VAE architecture and follow a paradigm where an encoder compresses videos into compact latents, and a deterministic decoder reconstructs the original videos from these latents.
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image
In the field of integrated circuit manufacturing, the detection and classification of nanoscale wafer defects are critical for subsequent root cause analysis and yield enhancement.
PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object Detection
Based on the views, a scale augmentation module and an angle acquisition module are constructed.
CodeV: Issue Resolving with Visual Data
Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks.
BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices
To be specific, we redesign the dynamic resolution scheme adopted by mainstream MLLMs and implement system optimization for hardware-aware deployment to optimize model inference on mobile phones.
ThinK: Thinner Key Cache by Query-Driven Pruning
Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications.
FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
Leveraging the power of LMMs, we introduce FabGPT, a customized IC fabrication large multimodal model for wafer defect knowledge query.
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
Large Language Models (LLMs) have become pivotal in advancing the field of artificial intelligence, yet their immense sizes pose significant challenges for both fine-tuning and deployment.
TerDiT: Ternary Diffusion Models with Transformers
Recent developments in large-scale pre-trained text-to-image diffusion models have significantly improved the generation of high-fidelity images, particularly with the emergence of diffusion models based on transformer architecture (DiTs).
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
Specifically, we propose, for the first time to our best knowledge, post-training approaches for task-agnostic and task-specific expert pruning and skipping of MoE LLMs, tailored to improve deployment efficiency while maintaining model performance across a wide range of tasks.
ImageBind-LLM: Multi-modality Instruction Tuning
During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder.
Zero-shot information extraction from radiological reports using ChatGPT
Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis.
From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding
By aligning the classes of previous datasets to our semantic space, we gather (image/video/skeleton/MoCap) datasets into a unified database in a unified label system, i. e., bridging "isolated islands" into a "Pangea".
Towards Multi-perspective conformance checking with fuzzy sets
This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process.
