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Found 12 papers, 10 papers with code
LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition
To address this, we present the Localization and Focus Vision Transformer (LF-ViT).
MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning
Learning multimodal representations involves integrating information from multiple heterogeneous sources of data.
Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications
We propose two lower bounds based on the amount of shared information between modalities and the disagreement between separately trained unimodal classifiers, and derive an upper bound through connections to approximate algorithms for min-entropy couplings.
Multimodal Fusion Interactions: A Study of Human and Automatic Quantification
In order to perform multimodal fusion of heterogeneous signals, we need to understand their interactions: how each modality individually provides information useful for a task and how this information changes in the presence of other modalities.
CamDiff: Camouflage Image Augmentation via Diffusion Model
Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt.
Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework
The recent explosion of interest in multimodal applications has resulted in a wide selection of datasets and methods for representing and integrating information from different modalities.
Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions
Previous GCNs-based methods usually require providing spatial correlation graph structure of observation sites in advance.
MultiBench: Multiscale Benchmarks for Multimodal Representation Learning
In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas.
AKG: Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations
Existing tensor compilers have proven their effectiveness in deploying deep neural networks on general-purpose hardware like CPU and GPU, but optimizing for neural processing units (NPUs) is still challenging due to the heterogeneous compute units and complicated memory hierarchy.
Optical manipulation of electronic dimensionality in a quantum material
Exotic phenomenon can be achieved in quantum materials by confining electronic states into two dimensions.
Strongly Correlated Electrons Materials Science Superconductivity
Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality
In this work, we estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring stations, training a long-term prediction model and comparing its predictions to measured values over the lockdown month. We show that our models achieve state-of-the-art performance on the data from air pollution measurement stations in Switzerland and in China: evaluate up to -15. 8% / +34. 4% change in NO2 / PM10 in Zurich; -35. 3 % / -3. 5 % and -42. 4 % / -34. 7 % in NO2 / PM2. 5 in Beijing and Wuhan respectively.
Adaptive Loss-aware Quantization for Multi-bit Networks
We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms.
