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Found 14 papers, 8 papers with code
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Auto-labels are most commonly generated via a two-stage approach -- first objects are detected and tracked over time, and then each object trajectory is passed to a learned refinement model to improve accuracy.
GePSAn: Generative Procedure Step Anticipation in Cooking Videos
While most previous work focus on the problem of data scarcity in procedural video datasets, another core challenge of future anticipation is how to account for multiple plausible future realizations in natural settings.
Self-Supervised Learning of Action Affordances as Interaction Modes
We show in the experiments that such affordance learning predicts interaction which covers most modes of interaction for the querying articulated object and can be fine-tuned to a goal-conditional model.
StepFormer: Self-supervised Step Discovery and Localization in Instructional Videos
This motivates the need to temporally localize the instruction steps in such videos, i. e. the task called key-step localization.
SAGE: Saliency-Guided Mixup with Optimal Rearrangements
For image classification, the most popular data augmentation techniques range from simple photometric and geometrical transformations, to more complex methods that use visual saliency to craft new training examples.
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge.
Graph2Vid: Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization
In this setup, we seek the optimal step ordering consistent with the procedure flow graph and a given video.
P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision
Our model is based on a transformer equipped with a memory module, which maps the start and goal observations to a sequence of plausible actions.
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
In our experiments, we show that Drop-DTW is a robust similarity measure for sequence retrieval and demonstrate its effectiveness as a training loss on diverse applications.
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples.
Ranked #4 on
Few-Shot Image Classification
on Meta-Dataset Rank
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification
Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples.
On the Importance of Visual Context for Data Augmentation in Scene Understanding
In this work, we consider object detection, semantic and instance segmentation and augment the training images by blending objects in existing scenes, using instance segmentation annotations.
Modeling Visual Context is Key to Augmenting Object Detection Datasets
For this approach to be successful, we show that modeling appropriately the visual context surrounding objects is crucial to place them in the right environment.
BlitzNet: A Real-Time Deep Network for Scene Understanding
Real-time scene understanding has become crucial in many applications such as autonomous driving.
Ranked #2 on
Real-Time Object Detection
on PASCAL VOC 2007
