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Found 12 papers, 8 papers with code
Focal and Efficient IOU Loss for Accurate Bounding Box Regression
(ii) Most of the loss functions ignore the imbalance problem in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR.
Learning Domain Invariant Representations for Generalizable Person Re-Identification
Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community.
Towards Principled Disentanglement for Domain Generalization
To tackle this challenge, we first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG).
Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation
Previous works on Treatment Effect Estimation (TEE) are not in widespread use because they are predominantly theoretical, where strong parametric assumptions are made but untractable for practical application.
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models
This survey is inspired by the remarkable progress in both computer vision and natural language processing, and recent trends shifting from single modality processing to multiple modality comprehension.
Domain-Specific Risk Minimization for Out-of-Distribution Generalization
Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target.
Evaluating Step-by-Step Reasoning through Symbolic Verification
Pre-trained language models (LMs) have shown remarkable reasoning performance using explanations or chain-of-thoughts (CoT)) for in-context learning.
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
In particular, when the adaptation target is a series of domains, the adaptation accuracy of AdaNPC is 50% higher than advanced TTA methods.
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.
A Study on the Calibration of In-context Learning
Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern language models (LMs).
Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors
In an effort to address these issues, we delve into the realm of zero-shot machine-generated text detection.
Debiasing Multimodal Large Language Models
In the realms of computer vision and natural language processing, Large Vision-Language Models (LVLMs) have become indispensable tools, proficient in generating textual descriptions based on visual inputs.
