Computer Vision

ZOPP: A groundbreaking framework for zero-shot offboard panoptic perception in autonomous driving, enabling high-quality 3D scene understanding without human labeling.

ZeroMark revolutionizes dataset ownership verification by enabling copyright protection without exposing watermarks, leveraging the intrinsic properties of DNNs trained on watermarked data.

Zero-to-Hero enhances zero-shot novel view synthesis by cleverly filtering attention maps during inference, achieving significantly higher fidelity and realism without retraining.

Zero-shot 3D scene reconstruction from single images is achieved by assembling diverse deep priors from large models, eliminating the need for 3D/2D training data and achieving superior performance.

MimicBrush: a novel image editing approach using reference imitation for intuitive zero-shot edits.

ZiRa achieves zero-shot generalizable incremental learning for vision-language object detection by using a memory-efficient dual-branch architecture and zero-interference loss, significantly boosting …

Zero-shot Event-Intensity Asymmetric Stereo (ZEST) uses visual prompting and monocular cues to achieve robust 3D perception without event-specific training, outperforming existing methods.

YOLA: A novel framework for object detection in low-light conditions, achieving significant improvements by learning illumination-invariant features through a novel module.

YOLOv10: Real-time object detection achieves state-of-the-art speed and accuracy by eliminating NMS post-processing and holistically optimizing model architecture for efficiency and accuracy.

WildGaussians enhances 3D Gaussian splatting for real-time rendering of photorealistic 3D scenes from in-the-wild images featuring occlusions and appearance changes.

Wild-GS achieves real-time novel view synthesis from unconstrained photos by efficiently adapting 3D Gaussian Splatting, significantly improving speed and quality over existing methods.

Unlocking personalized image retrieval and segmentation, a novel approach uses pre-trained text-to-image diffusion models to surpass supervised methods, addressing limitations of existing self-supervi…

Aligning vision models to human perceptual similarity judgments significantly boosts performance in diverse vision tasks like counting and segmentation, but surprisingly reduces performance in natural…

High-resolution datasets with diverse classes significantly improve the transferability of pretrained DNNs by reducing representation compression and mitigating the ’tunnel effect.'

LLM-powered data curation boosts web-scale visual entity recognition!

Wasserstein Distance-based Knowledge Distillation (WKD) rivals KL-divergence by leveraging rich category interrelations and handling non-overlapping distributions, significantly boosting performance i…

Warped Diffusion cleverly adapts image diffusion models for video inverse problems, solving flickering and temporal inconsistency issues by viewing video frames as continuous warping transformations a…

VQ-Map leverages vector quantization to estimate bird’s-eye-view maps with unprecedented accuracy, setting new benchmarks.

VPNet, a novel semantic scene completion network, uses multi-frame knowledge distillation and confident voxel proposals to improve accuracy and handle dynamic aspects of 3D scenes from point clouds, a…

VLG-CBM enhances concept bottleneck models with vision-language guidance for faithful interpretability and improved accuracy.