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Found 12 papers, 8 papers with code
MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction
MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time.
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc.
MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion
This paper introduces MVDiffusion, a simple yet effective method for generating consistent multi-view images from text prompts given pixel-to-pixel correspondences (e. g., perspective crops from a panorama or multi-view images given depth maps and poses).
NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization
State-of-the-art feature matching methods require each scene to be stored as a 3D point cloud with per-point features, consuming several gigabytes of storage per scene.
RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments
Visual relocalization has been a widely discussed problem in 3D vision: given a pre-constructed 3D visual map, the 6 DoF (Degrees-of-Freedom) pose of a query image is estimated.
QuadTree Attention for Vision Transformers
Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency.
Learning Camera Localization via Dense Scene Matching
We present a new method for scene agnostic camera localization using dense scene matching (DSM), where a cost volume is constructed between a query image and a scene.
Channel Equilibrium Networks for Learning Deep Representation
Unlike prior arts that simply removed the inhibited channels, we propose to "wake them up" during training by designing a novel neural building block, termed Channel Equilibrium (CE) block, which enables channels at the same layer to contribute equally to the learned representation.
Channel Equilibrium Networks
However, over-sparse CNNs have many collapsed channels (i. e. many channels with undesired zero values), impeding their learning ability.
Learning Efficient Detector with Semi-supervised Adaptive Distillation
ADL enlarges the distillation loss for hard-to-learn and hard-to-mimic samples and reduces distillation loss for the dominant easy samples, enabling distillation to work on the single-stage detector first time, even if the student and the teacher are identical.
Fast Video Shot Transition Localization with Deep Structured Models
In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos.
Ranked #3 on
Camera shot boundary detection
on ClipShots
(using extra training data)
Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation
This paper presents a novel ensemble framework to extract highly discriminative feature representation of image and its application for group-level happpiness intensity prediction in wild.
