Trending Research
Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems
Collaborative Simultaneous Localization And Mapping (C-SLAM) is a vital component for successful multi-robot operations in environments without an external positioning system, such as indoors, underground or underwater.
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models.
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
To reproduce the success of text-to-image (T2I) generation, recent works in text-to-video (T2V) generation employ large-scale text-video dataset for fine-tuning.
What Makes Good Examples for Visual In-Context Learning?
zhangyuanhan-ai/visual_prompt_retrieval
•
•
To overcome the problem, we propose a prompt retrieval framework to automate the selection of in-context examples.
Disentangling Random and Cyclic Effects in Time-Lapse Sequences
harskish/tlgan
•
•
We introduce the problem of disentangling time-lapse sequences in a way that allows separate, after-the-fact control of overall trends, cyclic effects, and random effects in the images, and describe a technique based on data-driven generative models that achieves this goal.
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
The fluency and factual knowledge of large language models (LLMs) heightens the need for corresponding systems to detect whether a piece of text is machine-written.
Interpretation of 3D CNNs for Brain MRI Data Classification
maxs-kan/InterpretableNeuroDL
•
•
Deep learning shows high potential for many medical image analysis tasks.
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
automatic1111/stable-diffusion-webui
•
•
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model.
Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images
zbzhzhy/gd-net
•
•
Then, we design a lightweight neural network with a multi-stage architecture to mimic the formed amended gradient descent process, in which efficient convolution and novel spectral zero-mean normalization are proposed to effectively extract spatial-spectral features for regressing an initialization, a basic gradient, and an incremental gradient.
Fine-Tuning Language Models from Human Preferences
lvwerra/trl
•
•
Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks.
