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Found 18 papers, 8 papers with code
DUSt3R: Geometric 3D Vision Made Easy
Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, relative and absolute camera.
MFOS: Model-Free & One-Shot Object Pose Estimation
Existing learning-based methods for object pose estimation in RGB images are mostly model-specific or category based.
Win-Win: Training High-Resolution Vision Transformers from Two Windows
It is 4 times faster to train than a full-resolution network, and it is straightforward to use at test time compared to existing approaches.
SACReg: Scene-Agnostic Coordinate Regression for Visual Localization
Instead of encoding the scene coordinates into the network weights, our model takes as input a database image with some sparse 2D pixel to 3D coordinate annotations, extracted from e. g. off-the-shelf Structure-from-Motion or RGB-D data, and a query image for which are predicted a dense 3D coordinate map and its confidence, based on cross-attention.
Robust Automatic Monocular Vehicle Speed Estimation for Traffic Surveillance
Experimental results conducted on three diverse benchmarks demonstrate excellent speed estimation accuracy that could enable the wide use of CCTV cameras for traffic analysis, even in challenging conditions where state-of-the-art methods completely fail.
SuperLoss: A Generic Loss for Robust Curriculum Learning
The key idea is to somehow estimate the importance (or weight) of each sample directly during training based on the observation that easy and hard samples behave differently and can therefore be separated.
R2D2: Reliable and Repeatable Detector and Descriptor
We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.
Ranked #2 on
Camera Localization
on Aachen Day-Night benchmark
Learning with Average Precision: Training Image Retrieval with a Listwise Loss
Recent deep models for image retrieval have outperformed traditional methods by leveraging ranking-tailored loss functions, but important theoretical and practical problems remain.
R2D2: Repeatable and Reliable Detector and Descriptor
In this work, we argue that salient regions are not necessarily discriminative, and therefore can harm the performance of the description.
Did It Change? Learning to Detect Point-Of-Interest Changes for Proactive Map Updates
Maps are an increasingly important tool in our daily lives, yet their rich semantic content still largely depends on manual input.
End-to-end Learning of Deep Visual Representations for Image Retrieval
Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it.
Ranked #13 on
Image Retrieval
on ROxford (Medium)
Deep Image Retrieval: Learning global representations for image search
We propose a novel approach for instance-level image retrieval.
Ranked #3 on
Image Retrieval
on Oxf105k
Beat-Event Detection in Action Movie Franchises
While important advances were recently made towards temporally localizing and recognizing specific human actions or activities in videos, efficient detection and classification of long video chunks belonging to semantically defined categories such as "pursuit" or "romance" remains challenging. We introduce a new dataset, Action Movie Franchises, consisting of a collection of Hollywood action movie franchises.
DeepMatching: Hierarchical Deformable Dense Matching
We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images.
Ranked #4 on
Dense Pixel Correspondence Estimation
on HPatches
Dense Pixel Correspondence Estimation
Optical Flow Estimation
Learning to Detect Motion Boundaries
We compare the results obtained with several state-of-the-art optical flow approaches and study the impact of the different cues used in the random forest. Furthermore, we introduce a new dataset, the YouTube Motion Boundaries dataset (YMB), that comprises 60 sequences taken from real-world videos with manually annotated motion boundaries.
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
We propose a novel approach for optical flow estimation , targeted at large displacements with significant oc-clusions.
Transformation Pursuit for Image Classification
We propose a principled algorithm Image Transformation Pursuit (ITP) for the automatic selection of a compact set of transformations.
Event Retrieval in Large Video Collections with Circulant Temporal Encoding
Furthermore, we extend product quantization to complex vectors in order to compress our descriptors, and to compare them in the compressed domain.
