1.2k citations · 2.7k across the 112 of their papers we have counts for
96 papers · 1 filter
Does Attention-Guided Masking Really Help Object Discovery in Object-Centric Learning?
Youliang Tao, Yanhua Han, Bin Zhao +3
Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention to aggregate image features…
Compressing AI Traffic: Standardized Neural Network Coding of Visual-Token Representations in Split Vision-Language Inference
Reza Heidari, Hamed R. Tavakoli, Juho Kannala
When the visual encoder and the language decoder of a vision-language model (VLM) run on different compute nodes, the intermediate visual-token embeddings become a communicated pay…
GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training
Yejun Zhang, Xinjue Wang, Zihan Wang +2
Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its accuracy still lags far behind…
SplatGuide: Geometric Priors from 3D Gaussians for Pose-Free Novel View Synthesis
Yejun Zhang, Zihan Wang, Xu Ji +8
Generating photorealistic novel views from unposed images requires both 3D geometric understanding and the ability to synthesize unseen content. A natural strategy combines feed-fo…
HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition
Neelu Madan, Rongzhen Zhao, Andreas Mogelmose +4
Slot attention is a powerful framework for object-centric learning, decomposing visual scenes into latent slots through iterative competitive attention. However, existing methods s…
Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization
Rongzhen Zhao, Zhiyuan Li, Juho Kannala +1
Video Object-Centric Learning (OCL) aims to represent objects as \textit{slot} vectors and maintain their consistency across frames. Slot-Slot Contrastive (SSC) loss has become the…
Cycle Consistency in Video Object-Centric Learning
Rongzhen Zhao, Zhiyuan Li, Ruonan Wei +2
Self-supervised video Object-Centric Learning (OCL) aims to discover distinct objects and associate them across time, whereas self-supervised Multi-Object Tracking (MOT) focuses on…
OP2GS: Object-Aware 3D Gaussian Splatting with Dual-Opacity Primitives
Guiyu Liu, Niklas Vaara, Janne Mustaniemi +2
3D Gaussian Splatting (3DGS) provides an explicit and efficient scene representation, but its primitives lack inherent object-level identity, hindering downstream tasks such as ope…
Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images
Matias Turkulainen, Akshay Krishnan, Filippo Aleotti +6
We present Cross-View Splatter, a feed-forward method that predicts pixel-aligned Gaussian splats for outdoor scenes captured at ground level AND by satellite. Faithful reconstruct…
Latent-Compressed Variational Autoencoder for Video Diffusion Models
Jiarui Guan, Wenshuai Zhao, Zhengtao Zou +2
Video variational autoencoders (VAEs) used in latent diffusion models typically require a sufficiently large number of latent channels to ensure high-quality video reconstruction.…
Scene-Agnostic Object-Centric Representation Learning for 3D Gaussian Splatting
Tsuheng Hsu, Guiyu Liu, Juho Kannala +1
Recent works on 3D scene understanding leverage 2D masks from visual foundation models (VFMs) to supervise radiance fields, enabling instance-level 3D segmentation. However, the su…
PAWS: Perception of Articulation in the Wild at Scale from Egocentric Videos
Yihao Wang, Yang Miao, Wenshuai Zhao +8
Articulation perception aims to recover the motion and structure of articulated objects (e.g., drawers and cupboards), and is fundamental to 3D scene understanding in robotics, sim…
3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization
Ulas Gunes, Matias Turkulainen, Mikhail Silaev +2
We present the first evaluation of 3D Gaussian Splatting methods on real fisheye imagery with fields of view above 180\textdegree{}. Our study evaluates Fisheye-GS \cite{liao2024fi…
Smoothing Slot Attention Iterations and Recurrences
Rongzhen Zhao, Wenyan Yang, Juho Kannala +1
Slot Attention (SA) lies at the heart of mainstream Object-Centric Learning (OCL). Image features can be aggregated into object-level representations by SA \textit{iteratively} ref…
Predicting Video Slot Attention Queries from Random Slot-Feature Pairs
Rongzhen Zhao, Jian Li, Juho Kannala +1
Unsupervised video Object-Centric Learning (OCL) is promising as it enables object-level scene representation and understanding as we humans do. Mainstream video OCL methods adopt…
Slot Attention with Re-Initialization and Self-Distillation
Rongzhen Zhao, Yi Zhao, Juho Kannala +1
Unlike popular solutions based on dense feature maps, Object-Centric Learning (OCL) represents visual scenes as sub-symbolic object-level feature vectors, termed slots, which are h…
FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition
Yinfan Wang, Jie Gui, Baosheng Yu +4
A major challenge in finger vein recognition is the lack of large-scale public datasets. Existing datasets contain few identities and limited samples per finger, restricting the ad…
FIORD: A Fisheye Indoor-Outdoor Dataset with LIDAR Ground Truth for 3D Scene Reconstruction and Benchmarking
Ulas Gunes, Matias Turkulainen, Xuqian Ren +3
The development of large-scale 3D scene reconstruction and novel view synthesis methods mostly rely on datasets comprising perspective images with narrow fields of view (FoV). Whil…
A Dataset for Semantic Segmentation in the Presence of Unknowns
Zakaria Laskar, Tomas Vojir, Matej Grcic +6
Before deployment in the real-world deep neural networks require thorough evaluation of how they handle both knowns, inputs represented in the training data, and unknowns (anomalie…
A2-GNN: Angle-Annular GNN for Visual Descriptor-free Camera Relocalization
Yejun Zhang, Shuzhe Wang, Juho Kannala
Visual localization involves estimating the 6-degree-of-freedom (6-DoF) camera pose within a known scene. A critical step in this process is identifying pixel-to-point corresponden…
Vector-Quantized Vision Foundation Models for Object-Centric Learning
Rongzhen Zhao, Vivienne Wang, Juho Kannala +1
Object-Centric Learning (OCL) aggregates image or video feature maps into object-level feature vectors, termed \textit{slots}. It's self-supervision of reconstructing the input fro…
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models
Hao Dong, Moru Liu, Kaiyang Zhou +4
In real-world scenarios, achieving domain adaptation and generalization poses significant challenges, as models must adapt to or generalize across unknown target distributions. Ext…
AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones
Xuqian Ren, Matias Turkulainen, Jiepeng Wang +4
Geometric priors are often used to enhance 3D reconstruction. With many smartphones featuring low-resolution depth sensors and the prevalence of off-the-shelf monocular geometry es…
Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization
Siyan Dong, Shuzhe Wang, Shaohui Liu +4
Visual localization aims to determine the camera pose of a query image relative to a database of posed images. In recent years, deep neural networks that directly regress camera po…