Search Results for author:
Found 9 papers, 5 papers with code
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers
We present COMEDIAN, a novel pipeline to initialize spatiotemporal transformers for action spotting, which involves self-supervised learning and knowledge distillation.
Ranked #1 on
Action Spotting
on SoccerNet-v2
VIBR: Learning View-Invariant Value Functions for Robust Visual Control
In this work, we tackle the problem of robust visual control at its core and present VIBR (View-Invariant Bellman Residuals), a method that combines multi-view training and invariant prediction to reduce out-of-distribution (OOD) generalization gap for RL based visuomotor control.
Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning
A good data representation should contain relations between the instances, or semantic similarity and dissimilarity, that contrastive learning harms by considering all negatives as noise.
Ranked #1 on
Self-supervised Video Retrieval
on HMDB51
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks
We present a new self-supervised pre-training of Vision Transformers for dense prediction tasks.
End-to-end Person Search Sequentially Trained on Aggregated Dataset
In video surveillance applications, person search is a challenging task consisting in detecting people and extracting features from their silhouette for re-identification (re-ID) purpose.
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning
To circumvent this issue, we propose a novel formulation of contrastive learning using semantic similarity between instances called Similarity Contrastive Estimation (SCE).
Ranked #70 on
Self-Supervised Image Classification
on ImageNet
Evaluating Robustness over High Level Driving Instruction for Autonomous Driving
In recent years, we have witnessed increasingly high performance in the field of autonomous end-to-end driving.
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image.
Ranked #2 on
Vehicle Pose Estimation
on KITTI Cars Hard
(using extra training data)
