Search Results for author:
Found 8 papers, 6 papers with code
Into the Fog: Evaluating Multiple Object Tracking Robustness
To address these limitations, we propose a pipeline for physic-based volumetric fog simulation in arbitrary real-world MOT dataset utilizing frame-by-frame monocular depth estimation and a fog formation optical model.
Towards Multimodal In-Context Learning for Vision & Language Models
Inspired by the emergence of Large Language Models (LLMs) that can truly understand human language, significant progress has been made in aligning other, non-language, modalities to be `understandable' by an LLM, primarily via converting their samples into a sequence of embedded language-like tokens directly fed into the LLM (decoder) input stream.
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs
Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language Models (VLMs).
TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification
Vision and Language Models (VLMs), such as CLIP, have enabled visual recognition of a potentially unlimited set of categories described by text prompts.
Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions
We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i. e. test time) when the respective weather conditions are encountered.
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions
This catastrophic forgetting is typically addressed via incremental learning approaches which usually re-train the model by either keeping a memory bank of training samples or keeping a copy of the entire model or model parameters for each scenario.
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization
This can be a hurdle in fields which require continuous dynamic adaptation or suffer from scarcity of data, e. g. autonomous driving in challenging weather conditions.
