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Found 45 papers, 20 papers with code
Revisiting PCA for time series reduction in temporal dimension
Revisiting PCA for Time Series Reduction in Temporal Dimension; Jiaxin Gao, Wenbo Hu, Yuntian Chen; Deep learning has significantly advanced time series analysis (TSA), enabling the extraction of complex patterns for tasks like classification, forecasting, and regression.
SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task Linkage
It then employs a simple assistive task such as a masked language model task or an element lookup by position task to encode the semantics of the masked keywords.
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models
This paper investigates representative MLLMs, focusing on their calibration across various scenarios, including before and after visual fine-tuning, as well as before and after multimodal training of the base LLMs.
NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images
Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data.
Verbalized Representation Learning for Interpretable Few-Shot Generalization
Humans recognize objects after observing only a few examples, a remarkable capability enabled by their inherent language understanding of the real-world environment.
TimeFormer: Capturing Temporal Relationships of Deformable 3D Gaussians for Robust Reconstruction
To address the above issue, we design a plug-and-play module called TimeFormer to enable existing deformable 3D Gaussians reconstruction methods with the ability to implicitly model motion patterns from a learning perspective.
MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
In this paper, we introduce a multimodal retrieval-augmented generation benchmark, MRAG-Bench, in which we systematically identify and categorize scenarios where visually augmented knowledge is better than textual knowledge, for instance, more images from varying viewpoints.
StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos
This paper presents a novel framework for converting 2D videos to immersive stereoscopic 3D, addressing the growing demand for 3D content in immersive experience.
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames with precise camera pose control.
DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets.
CV-VAE: A Compatible Video VAE for Latent Generative Video Models
Moreover, since current diffusion-based approaches are often implemented using pre-trained text-to-image (T2I) models, directly training a video VAE without considering the compatibility with existing T2I models will result in a latent space gap between them, which will take huge computational resources for training to bridge the gap even with the T2I models as initialization.
Matryoshka Query Transformer for Large Vision-Language Models
This raises the question: can we achieve flexibility in the number of visual tokens to suit different tasks and computational resources?
Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh
This approach reduces the need to design various algorithms for different types of Gaussian manipulation.
Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids
Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge to effectively and efficiently characterize anisotropic areas induced by the cone-casting procedure.
VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
Our work also highlights the critical balance between faithfulness and coverage of model outputs, and encourages future works to address hallucinations in LVLMs while keeping their outputs informative.
Inverse Rendering of Glossy Objects via the Neural Plenoptic Function and Radiance Fields
Our method has two stages: the geometry of the target object and the pre-filtered environmental radiance fields are reconstructed in the first stage, and materials of the target object are estimated in the second stage with the proposed NeP and material-aware cone sampling strategy.
Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results while advancing real-time rendering performance.
Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration
This is because 3DGS treats each pixel as an isolated, single point rather than as an area, causing insensitivity to changes in the footprints of pixels.
Texture-GS: Disentangling the Geometry and Texture for 3D Gaussian Splatting Editing
3D Gaussian splatting, emerging as a groundbreaking approach, has drawn increasing attention for its capabilities of high-fidelity reconstruction and real-time rendering.
Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series
Our method not only produces accurate imputations that is robust to high missing rates, but also is computationally efficient due to the fast training of its non-generative model.
DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagation
To ensure meaningful and aligned textures to the scene, we develop a novel coarse-to-fine panoramic texture generation approach with dual texture alignment, which both considers the geometry and texture cues of the captured scenes.
Investigating Uncertainty Calibration of Aligned Language Models under the Multiple-Choice Setting
Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-trained LMs.
HiFi-123: Towards High-fidelity One Image to 3D Content Generation
Our contributions are twofold: First, we propose a Reference-Guided Novel View Enhancement (RGNV) technique that significantly improves the fidelity of diffusion-based zero-shot novel view synthesis methods.
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
BLIVA demonstrates significant capability in decoding real-world images, irrespective of text presence.
Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields
Despite the tremendous progress in neural radiance fields (NeRF), we still face a dilemma of the trade-off between quality and efficiency, e. g., MipNeRF presents fine-detailed and anti-aliased renderings but takes days for training, while Instant-ngp can accomplish the reconstruction in a few minutes but suffers from blurring or aliasing when rendering at various distances or resolutions due to ignoring the sampling area.
Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
Long-term time series forecasting (LTSF) is a crucial aspect of modern society, playing a pivotal role in facilitating long-term planning and developing early warning systems.
Iterative Adversarial Attack on Image-guided Story Ending Generation
Multimodal learning involves developing models that can integrate information from various sources like images and texts.
VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts
In the field of historical manuscript research, scholars frequently encounter novel symbols in ancient texts, investing considerable effort in their identification and documentation.
Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
To address this, we evaluate the quality of propensity scores from the perspective of uncertainty calibration, proposing the use of expected calibration error (ECE) as a measure of propensity-score quality.
Disentangled Image Colorization via Global Anchors
Our key insight is that several carefully located anchors could approximately represent the color distribution of an image, and conditioned on the anchor colors, we can predict the image color in a deterministic manner by utilizing internal correlation.
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy.
SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellite
However, existing public datasets do not include images obtained by drones at different heights, and the types of scenes are relatively homogeneous, which yields issues in assessing a model's capability to adapt to complex and changing scenes.
Scale-arbitrary Invertible Image Downscaling
In this paper, we propose a scale-Arbitrary Invertible image Downscaling Network (AIDN), to natively downscale HR images with arbitrary scale factors.
Conditional Directed Graph Convolution for 3D Human Pose Estimation
Based on this representation, we further propose a spatial-temporal conditional directed graph convolution to leverage varying non-local dependence for different poses by conditioning the graph topology on input poses.
Ranked #17 on
3D Human Pose Estimation
on Human3.6M
Sparse Needlets for Lighting Estimation with Spherical Transport Loss
Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting.
Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations
Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed.
StackVAE-G: An efficient and interpretable model for time series anomaly detection
We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels.
Accurate and Reliable Forecasting using Stochastic Differential Equations
It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments.
Bidirectional Projection Network for Cross Dimension Scene Understanding
Via the \emph{BPM}, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition.
Ranked #19 on
Semantic Segmentation
on ScanNet
GMLight: Lighting Estimation via Geometric Distribution Approximation
This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation.
Deep Halftoning With Reversible Binary Pattern
Existing halftoning algorithms usually drop colors and fine details when dithering color images with binary dot patterns, which makes it extremely difficult to recover the original information.
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models.
Mononizing Binocular Videos
This paper presents the idea ofmono-nizingbinocular videos and a frame-work to effectively realize it.
Image and Video Processing Graphics
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation.
Fast Sampling for Bayesian Max-Margin Models
Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowdsourcing, and they conjoin the flexibility of Bayesian modeling and predictive strengths of max-margin learning.
