Trending Research
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting.
Better Call SAL: Towards Learning to Segment Anything in Lidar
We propose the SAL (Segment Anything in Lidar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision.
MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents
MOSS ensures consistency and adaptability by using a mechanism that maintains the Python context across interactions, including isolation of local variables and preservation of runtime integrity.
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
VikParuchuri/surya
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NeurIPS 2021
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders.
Ranked #1 on
Semantic Segmentation
on COCO-Stuff full
PuLID: Pure and Lightning ID Customization via Contrastive Alignment
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation.
PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
Specifically, PrefixQuant identifies high-frequency outlier tokens and prefixes them in the KV cache, preventing the generation of outlier tokens during inference and simplifying quantization.
A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation
chenllliang/dnd-transformer
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This work tackles the information loss bottleneck of vector-quantization (VQ) autoregressive image generation by introducing a novel model architecture called the 2-Dimensional Autoregression (DnD) Transformer.
SyllableLM: Learning Coarse Semantic Units for Speech Language Models
For speech in particular, the high resolution of waveforms (16, 000 samples/second or more) presents a significant challenge as speech-based language models have had to use several times more tokens per word than text-based language models.
SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning
With support of over $300+$ LLMs and $50+$ MLLMs, SWIFT stands as the open-source framework that provide the most comprehensive support for fine-tuning large models.
