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Found 21 papers, 6 papers with code
Where and when to look? Spatial-temporal attention for action recognition in videos
Our model is efficient, as it proposes a separable spatio-temporal mechanism for video attention, while being able to identify important parts of the video both spatially and temporally.
Action Recognition In Videos
Temporal Action Localization
+1
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
Machine-based prediction of real-world events is garnering attention due to its potential for informed decision-making.
ConR: Contrastive Regularizer for Deep Imbalanced Regression
ConR addresses the continuous nature of label space with two main strategies in a contrastive manner: incorrect proximities are penalized proportionate to the label similarities and the correct ones are encouraged to model local similarities.
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
Despite these advances, there remains a significant gap in understanding the impact of different SSCL strategies on time series forecasting performance, as well as the specific benefits that SSCL can bring.
GAN-based Image Compression with Improved RDO Process
GAN-based image compression schemes have shown remarkable progress lately due to their high perceptual quality at low bit rates.
Robust Reinforcement Learning Objectives for Sequential Recommender Systems
Attention-based sequential recommendation methods have demonstrated promising results by accurately capturing users' dynamic interests from historical interactions.
JND-Based Perceptual Optimization For Learned Image Compression
After combining them together, we can better assign the distortion in the compressed image with the guidance of JND to preserve the high perceptual quality.
HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation
However, they have a major drawback that the generated JND is assessed in the real-world signal domain instead of in the perceptual domain in the human brain.
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
The performance of time series forecasting has recently been greatly improved by the introduction of transformers.
Full RGB Just Noticeable Difference (JND) Modelling
Besides, the JND of the red and blue channels are larger than that of the green one according to the experimental results of the proposed model, which demonstrates that more changes can be tolerated in the red and blue channels, in line with the well-known fact that the human visual system is more sensitive to the green channel in comparison with the red and blue ones.
Auto-Weighted Layer Representation Based View Synthesis Distortion Estimation for 3-D Video Coding
Experimental results show that the VSD can be accurately estimated with the weights learnt by the nonlinear mapping function once its associated S-VSDs are available.
A New Image Codec Paradigm for Human and Machine Uses
Meanwhile, an image predictor is designed and trained to achieve the general-quality image reconstruction with the 16-bit gray-scale profile and signal features.
Continuous Graph Flow
In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data.
Image Generation from Layout
The representation of each object is disentangled into a specified/certain part (category) and an unspecified/uncertain part (appearance).
Ranked #2 on
Layout-to-Image Generation
on Visual Genome 64x64
Interpretable Spatio-temporal Attention for Video Action Recognition
Inspired by the observation that humans are able to process videos efficiently by only paying attention where and when it is needed, we propose an interpretable and easy plug-in spatial-temporal attention mechanism for video action recognition.
Generating Handwritten Chinese Characters using CycleGAN
Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values.
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection.
Multi-level Residual Networks from Dynamical Systems View
Deep residual networks (ResNets) and their variants are widely used in many computer vision applications and natural language processing tasks.
Backtracking Regression Forests for Accurate Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success.
Ranked #49 on
Image Classification
on STL-10
Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing
Both frameworks employ the division of gradient and the local activity measurement to achieve noise removal.
