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Found 24 papers, 9 papers with code
PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
We introduce the Proportional Payoff Allocation Game (PPA-Game) to model how agents, akin to content creators on platforms like YouTube and TikTok, compete for divisible resources and consumers' attention.
A Survey on Evaluation of Out-of-Distribution Generalization
In closing, we propose several promising directions for future research in OOD evaluation.
Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection
This paper presents Incremental Vision-Language Object Detection (IVLOD), a novel learning task designed to incrementally adapt pre-trained Vision-Language Object Detection Models (VLODMs) to various specialized domains, while simultaneously preserving their zero-shot generalization capabilities for the generalized domain.
On the Out-Of-Distribution Generalization of Multimodal Large Language Models
We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
TouchStone: Evaluating Vision-Language Models by Language Models
Large vision-language models (LVLMs) have recently witnessed rapid advancements, exhibiting a remarkable capacity for perceiving, understanding, and processing visual information by connecting visual receptor with large language models (LLMs).
Flatness-Aware Minimization for Domain Generalization
However, we reveal that Adam is not necessarily the optimal choice for the majority of current DG methods and datasets.
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
In reality, agents often have to learn and maximize the rewards of the resources at the same time.
Meta Adaptive Task Sampling for Few-Domain Generalization
Moreover, we propose a Meta Adaptive Task Sampling (MATS) procedure to differentiate base tasks according to their semantic and domain-shift similarity to the novel task.
Rethinking the Evaluation Protocol of Domain Generalization
This paper examines the risks of test data information leakage from two aspects of the current evaluation protocol: supervised pretraining on ImageNet and oracle model selection.
Exploring and Exploiting Data Heterogeneity in Recommendation
In this work, we focus on exploring two representative categories of heterogeneity in recommendation data that is the heterogeneity of prediction mechanism and covariate distribution and propose an algorithm that explores the heterogeneity through a bilevel clustering method.
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization
Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i. e., the worst-case loss within a perturbation radius).
Stable Learning via Sparse Variable Independence
The problem of covariate-shift generalization has attracted intensive research attention.
Product Ranking for Revenue Maximization with Multiple Purchases
In this paper, we assume that each consumer can purchase multiple products at will.
NICO++: Towards Better Benchmarking for Domain Generalization
Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.
Towards Domain Generalization in Object Detection
Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied.
Regulatory Instruments for Fair Personalized Pricing
Personalized pricing is a business strategy to charge different prices to individual consumers based on their characteristics and behaviors.
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
Afterward, we prove that under ideal conditions, independence-driven importance weighting algorithms could identify the variables in this set.
Towards Out-Of-Distribution Generalization: A Survey
This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field.
Towards Unsupervised Domain Generalization
Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains.
Deep Stable Learning for Out-Of-Distribution Generalization
Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise.
Ranked #28 on
Domain Generalization
on VLCS
Sample Balancing for Improving Generalization under Distribution Shifts
We propose to address this problem by removing the dependencies between features via reweighting training samples, which results in a more balanced distribution and helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between features and labels.
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation
To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes from them.
Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading
Current state-of-the-art approaches for lip reading are based on sequence-to-sequence architectures that are designed for natural machine translation and audio speech recognition.
Ranked #13 on
Lipreading
on LRS2
(using extra training data)
