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Found 29 papers, 13 papers with code
MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression
In this paper, we present MLICv2 and MLICv2$^+$, enhanced versions of the MLIC series, featuring improved transform techniques, entropy modeling, and instance adaptability.
L-LBVC: Long-Term Motion Estimation and Prediction for Learned Bi-Directional Video Compression
Recently, learned video compression (LVC) has shown superior performance under low-delay configuration.
Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction
Specifically, we propose a spatial condition-based prediction module to utilize the grid-captured scene information for prediction, with a residual compensation strategy designed to learn the missing fine-grained information.
Towards Reliable Time Series Forecasting under Future Uncertainty: Ambiguity and Novelty Rejection Mechanisms
In real-world time series forecasting, uncertainty and lack of reliable evaluation pose significant challenges.
Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting
Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience.
CDI3D: Cross-guided Dense-view Interpolation for 3D Reconstruction
However, challenges remain as 2D diffusion models often struggle to produce dense images with strong multi-view consistency, and LRMs tend to amplify these inconsistencies during the 3D reconstruction process.
Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation
This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts, including single images, multi-view images, and text descriptions.
DoubleDiffusion: Combining Heat Diffusion with Denoising Diffusion for Texture Generation on 3D Meshes
This paper addresses the problem of generating textures for 3D mesh assets.
Hybrid Local-Global Context Learning for Neural Video Compression
In this paper, we propose a hybrid context generation module, which combines the advantages of the above methods in an optimal way and achieves accurate compensation at a low bit cost.
Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models
Among XAI techniques, Concept Bottleneck Models (CBMs) enhance transparency by using high-level semantic concepts.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Learning New Concepts, Remembering the Old: A Novel Continual Learning
Concept Bottleneck Models (CBMs) enhance model interpretability by introducing human-understandable concepts within the architecture.
Maintaining Informative Coherence: Migrating Hallucinations in Large Language Models via Absorbing Markov Chains
Large Language Models (LLMs) are powerful tools for text generation, translation, and summarization, but they often suffer from hallucinations-instances where they fail to maintain the fidelity and coherence of contextual information during decoding, sometimes overlooking critical details due to their sampling strategies and inherent biases from training data and fine-tuning discrepancies.
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models
Despite the transformative impact of deep learning across multiple domains, the inherent opacity of these models has driven the development of Explainable Artificial Intelligence (XAI).
TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks
Our results validate the effectiveness of our approach in addressing the key challenges in time series forecasting, paving the way for more reliable and efficient predictive models in practical applications.
UCVC: A Unified Contextual Video Compression Framework with Joint P-frame and B-frame Coding
This paper presents a learned video compression method in response to video compression track of the 6th Challenge on Learned Image Compression (CLIC), at DCC 2024. Specifically, we propose a unified contextual video compression framework (UCVC) for joint P-frame and B-frame coding.
ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion
Given a single image of a 3D object, this paper proposes a novel method (named ConsistNet) that is able to generate multiple images of the same object, as if seen they are captured from different viewpoints, while the 3D (multi-view) consistencies among those multiple generated images are effectively exploited.
A Comprehensive Review of Community Detection in Graphs
This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based method, spectral clustering, probabilistic modelling, and deep learning.
Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended Reality
One of the major difficulties is the lack of high-quality indoor video stereo training datasets captured by head-mounted VR/AR glasses.
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
Additionally, to capture global contexts, we propose the linear complexity attention-based global correlations capturing by leveraging the decomposition of the softmax operation.
Ranked #1 on
Image Compression
on kodak
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View
In contrast, we propose to use parametric depth distribution modeling for feature transformation.
LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression
To tackle this issue, we propose Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression (LLIC).
Butterfly: Multiple Reference Frames Feature Propagation Mechanism for Neural Video Compression
Using more reference frames can significantly improve the compression efficiency in neural video compression.
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's-Eye View
In contrast, we propose to use parametric depth distribution modeling for feature transformation.
MLIC: Multi-Reference Entropy Model for Learned Image Compression
Based on MEM and MEM$^+$, we propose image compression models MLIC and MLIC$^+$.
Ranked #1 on
Image Compression
on kodak
Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo
Boundary pixels usually follow a multi-modal distribution as they represent different depths; Therefore, the assumption results in an erroneous depth prediction at the coarser level of the cost volume pyramid and can not be corrected in the refinement levels leading to wrong depth predictions.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Self-supervised Learning of Depth Inference for Multi-view Stereo
Here, we propose a self-supervised learning framework for multi-view stereo that exploit pseudo labels from the input data.
Super-Resolving Compressed Video in Coding Chain
Scaling and lossy coding are widely used in video transmission and storage.
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
We propose a cost volume-based neural network for depth inference from multi-view images.
Ranked #15 on
3D Reconstruction
on DTU
