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Found 37 papers, 16 papers with code
Multi-Scale Semantic Segmentation with Modified MBConv Blocks
Recently, MBConv blocks, initially designed for efficiency in resource-limited settings and later adapted for cutting-edge image classification performances, have demonstrated significant potential in image classification tasks.
NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
NestE represents each atomic fact as a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix that rotates the $1\times3$ atomic fact matrix through matrix multiplication.
TD-Net: A Tri-domain network for sparse-view CT reconstruction
Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks, frequently suffers from image quality degradation, manifested as noise and artifacts.
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods.
INeAT: Iterative Neural Adaptive Tomography
Here, we propose a neural rendering method for CT reconstruction, named Iterative Neural Adaptive Tomography (INeAT), which incorporates iterative posture optimization to effectively counteract the influence of posture perturbations in data, particularly in cases involving significant posture variations.
PC-bzip2: a phase-space continuity enhanced lossless compression algorithm for light field microscopy data
Moreover, by introducing the temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.
ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning
Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs).
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.
Shrinking Embeddings for Hyper-Relational Knowledge Graphs
Although some recent works have proposed to embed hyper-relational KGs, these methods fail to capture essential inference patterns of hyper-relational facts such as qualifier monotonicity, qualifier implication, and qualifier mutual exclusion, limiting their generalization capability.
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
MMViT: Multiscale Multiview Vision Transformers
Our model encodes different views of the input signal and builds several channel-resolution feature stages to process the multiple views of the input at different resolutions in parallel.
Geometric Relational Embeddings: A Survey
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Hierarchical Multi-label Classification
Knowledge Graph Completion
+1
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system.
Modeling Relational Patterns for Logical Query Answering over Knowledge Graphs
However, the role of such patterns in answering FOL queries by query embedding models has not been yet studied in the literature.
Reversible Vision Transformers
Reversible Vision Transformers achieve a reduced memory footprint of up to 15. 5x at roughly identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for hardware resource limited training regimes.
Normalized Contrastive Learning for Text-Video Retrieval
Cross-modal contrastive learning has led the recent advances in multimodal retrieval with its simplicity and effectiveness.
Ultrahyperbolic Knowledge Graph Embeddings
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.
Faithiful Embeddings for EL++ Knowledge Bases
Namely, the learned model of BoxEL embedding with loss 0 is a (logical) model of the KB.
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration.
Ranked #3 on
Action Anticipation
on EPIC-KITCHENS-100
(using extra training data)
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection.
Ranked #1 on
Action Classification
on Kinetics-600
(GFLOPs metric)
PyTorchVideo: A Deep Learning Library for Video Understanding
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing.
Pseudo-Riemannian Graph Convolutional Networks
Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning
We present a large-scale study on unsupervised spatiotemporal representation learning from videos.
Ranked #3 on
Self-Supervised Action Recognition
on HMDB51
Representation Learning
Self-Supervised Action Recognition
+1
Multiscale Vision Transformers
We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10x more costly in computation and parameters.
Ranked #14 on
Action Classification
on Charades
Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos
We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets.
Multiview Pseudo-Labeling for Semi-supervised Learning from Video
We present a multiview pseudo-labeling approach to video learning, a novel framework that uses complementary views in the form of appearance and motion information for semi-supervised learning in video.
MOFA: Modular Factorial Design for Hyperparameter Optimization
MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter space by factorial design and exploitation of evaluation results by factorial analysis.
Less is More: Learning Highlight Detection from Video Duration
Highlight detection has the potential to significantly ease video browsing, but existing methods often suffer from expensive supervision requirements, where human viewers must manually identify highlights in training videos.
Snap Angle Prediction for 360° Panoramas
360° panoramas are a rich medium, yet notoriously difficult to visualize in the 2D image plane.
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos
We propose an end-to-end learning framework for segmenting generic objects in both images and videos.
Snap Angle Prediction for 360$^{\circ}$ Panoramas
360$^{\circ}$ panoramas are a rich medium, yet notoriously difficult to visualize in the 2D image plane.
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition.
FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos
Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos.
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)
We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems.
Pixel Objectness
We propose an end-to-end learning framework for generating foreground object segmentations.
FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos.
Storyline Representation of Egocentric Videos With an Applications to Story-Based Search
To address this, we propose a storyline representation that expresses an egocentric video as a set of jointly inferred, through MRF inference, story elements comprising of actors, locations, supporting objects and events, depicted on a timeline.
