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Found 13 papers, 6 papers with code
PoseAnimate: Zero-shot high fidelity pose controllable character animation
Image-to-video(I2V) generation aims to create a video sequence from a single image, which requires high temporal coherence and visual fidelity with the source image. However, existing approaches suffer from character appearance inconsistency and poor preservation of fine details.
LoopAnimate: Loopable Salient Object Animation
The proposed LoopAnimate, which for the first time extends the single-pass generation length of UNet-based video generation models to 35 frames while maintaining high-quality video generation.
ADMap: Anti-disturbance framework for reconstructing online vectorized HD map
Therefore, this paper proposes the Anti-disturbance Map reconstruction framework (ADMap).
Lightweight high-resolution Subject Matting in the Real World
To alleviate these issues, we propose to construct a saliency object matting dataset HRSOM and a lightweight network PSUNet.
BARET : Balanced Attention based Real image Editing driven by Target-text Inversion
Image editing approaches with diffusion models have been rapidly developed, yet their applicability are subject to requirements such as specific editing types (e. g., foreground or background object editing, style transfer), multiple conditions (e. g., mask, sketch, caption), and time consuming fine-tuning of diffusion models.
u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
Recent advancements in multi-modal large language models (MLLMs) have led to substantial improvements in visual understanding, primarily driven by sophisticated modality alignment strategies.
A Machine Vision Method for Correction of Eccentric Error: Based on Adaptive Enhancement Algorithm
Focusing on the severe defocus blur of reference crosshair image caused by the imaging characteristic of the aspherical optical element, which may lead to the failure of correction, an Adaptive Enhancement Algorithm (AEA) is proposed to strengthen the crosshair image.
All-pairs Consistency Learning for Weakly Supervised Semantic Segmentation
Given a pair of augmented views, our approach regularizes the activation intensities between a pair of augmented views, while also ensuring that the affinity across regions within each view remains consistent.
Ranked #15 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Object Localization
Weakly supervised Semantic Segmentation
+1
EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection
In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method.
Ranked #1 on
3D Object Detection
on nuScenes
GAM : Gradient Attention Module of Optimization for Point Clouds Analysis
In point cloud analysis tasks, the existing local feature aggregation descriptors (LFAD) are unable to fully utilize information in the neighborhood of central points.
VA-GCN: A Vector Attention Graph Convolution Network for learning on Point Clouds
Owing to the development of research on local aggregation operators, dramatic breakthrough has been made in point cloud analysis models.
A De-raining semantic segmentation network for real-time foreground segmentation
According to the control experiments, the performances of MultiScaleSE Block and Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively are improved to a certain degree on the Foreground Accuracy index.
BAM: A Balanced Attention Mechanism for Single Image Super Resolution
The results demonstrate that BAM can efficiently improve the networks performance, and for those originally with attention mechanism, the substitution with BAM further reduces the amount of parameters and increases the inference speed.
