Computer Vision

eFreeSplat: a novel, epipolar-free 3D Gaussian splatting model for generalizable novel view synthesis, surpassing state-of-the-art methods by achieving superior geometry reconstruction and novel view …

EnsIR: Training-free image restoration ensemble via Gaussian mixture models, boosting accuracy efficiently.

DiChaViT boosts channel-adaptive vision transformers by enhancing feature diversity, yielding a 1.5-5% accuracy gain over state-of-the-art MCI models on diverse datasets.

This paper introduces a novel post-processing technique that significantly boosts the perceptual quality of images generated by consistency models using a joint classifier-discriminator adversarially …

Optical-flow-free video semantic segmentation excels in adverse weather by merging adjacent frame information via a fusion block and a novel temporal-spatial teacher-student learning strategy.

EfficientNAT: a novel approach to token-based image synthesis boosts performance and slashes computational costs by cleverly disentangling and optimizing spatial-temporal interactions between image to…

EMVP: A novel PEFT pipeline boosts Visual Place Recognition accuracy by 97.6% using Centroid-Free Probing & Dynamic Power Normalization, saving 64.3% of parameters.

EM Distillation (EMD) efficiently trains one-step diffusion models by using an Expectation-Maximization approach, achieving state-of-the-art image generation quality and outperforming existing methods…

Elucidating Dataset Condensation (EDC) achieves state-of-the-art accuracy in dataset condensation by implementing soft category-aware matching and a smoothing learning rate schedule, improving model t…

EGSST: a novel framework for event-based object detection, uses graph structures and transformers to efficiently process event data, achieving high accuracy and speed in dynamic scenes.

EgoChoir: a novel framework harmonizes visual appearance, head motion, and 3D objects to accurately estimate 3D human contact and object affordance from egocentric videos, surpassing existing methods.

EfficientCAPER: A novel end-to-end framework achieves fast & robust category-level articulated object pose estimation by using a joint-centric approach, eliminating post-processing optimization and en…

BaFormer: a novel boundary-aware Transformer network achieves efficient and accurate temporal action segmentation by using instance and global queries for segment classification and boundary predictio…

Sort & Search: 1000x faster lifelong model evaluation!

Boosting Vision Transformer adaptation! Householder Transformation-based Adaptor (HTA) outperforms existing methods by dynamically adjusting adaptation matrix ranks across layers, improving efficiency…

Effective rank regularization enhances 3D Gaussian splatting, resolving needle-like artifacts and improving 3D model quality.

EEG2Video reconstructs dynamic videos from EEG signals, achieving 79.8% accuracy in semantic classification and 0.256 SSIM in video reconstruction.

The Efficient Diffusion Transformer (EDT) framework significantly speeds up and improves image generation by leveraging a lightweight architecture, human-like sketching-inspired Attention Modulation M…

ECMamba: A novel dual-branch framework efficiently corrects multiple exposure images by integrating Retinex theory and an innovative 2D selective state-space layer, achieving state-of-the-art performa…

EAGLE: A novel unsupervised cross-view adaptation method for semantic segmentation achieves state-of-the-art performance by efficiently modeling geometric structural changes across different camera vi…