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Found 7 papers, 3 papers with code
TinyLLaVA: A Framework of Small-scale Large Multimodal Models
We present the TinyLLaVA framework that provides a unified perspective in designing and analyzing the small-scale Large Multimodal Models (LMMs).
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Visual Question Answering
on MM-Vet
Modulate Your Spectrum in Self-Supervised Learning
In this work, we introduce Spectral Transformation (ST), a framework to modulate the spectrum of embedding and to seek for functions beyond whitening that can avoid dimensional collapse.
Optimal Pricing Schemes in the Presence of Social Learning and Costly Reporting
A monopoly platform sells either a risky product (with unknown utility) or a safe product (with known utility) to agents who sequentially arrive and learn the utility of the risky product by the reporting of previous agents.
An Investigation into Whitening Loss for Self-supervised Learning
A desirable objective in self-supervised learning (SSL) is to avoid feature collapse.
Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes
This shows that DMA-Net provides a good tradeoff between segmentation quality and speed for semantic segmentation in street scenes.
Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes
In this paper, we present a novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes.
A VCG-based Fair Incentive Mechanism for Federated Learning
In order to build an ecosystem for FL to operate in a sustainable manner, it has to be economically attractive to data owners.
Computer Science and Game Theory
