Search Results for author:
Found 31 papers, 20 papers with code
Exploring Transferability for Randomized Smoothing
However, the model robustness, which is a critical aspect for safety, is often optimized for each specific task rather than at the pretraining stage.
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
Associative Transformer
Existing studies such as the Coordination method employ iterative cross-attention mechanisms with a bottleneck to enable the sparse association of inputs.
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
The Gestalt law of common fate, i. e., what move at the same speed belong together, has inspired unsupervised object discovery based on motion segmentation.
Ranked #1 on
Unsupervised Object Segmentation
on FBMS-59
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part.
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy
Previous methods in unsupervised video object segmentation (UVOS) have demonstrated the effectiveness of motion as either input or supervision for segmentation.
ClipCrop: Conditioned Cropping Driven by Vision-Language Model
Image cropping has progressed tremendously under the data-driven paradigm.
Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance
Existing solutions to this problem estimate a single image sequence without considering the motion ambiguity for each region.
Extreme Masking for Learning Instance and Distributed Visual Representations
The paper presents a scalable approach for learning spatially distributed visual representations over individual tokens and a holistic instance representation simultaneously.
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion
In this paper, instead of two consecutive frames, we propose to exploit a pair of images captured by dual RS cameras with reversed RS directions for this highly challenging task.
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
Concretely, we pretrain the sign-to-gloss visual network on the general domain of human actions and the within-domain of a sign-to-gloss dataset, and pretrain the gloss-to-text translation network on the general domain of a multilingual corpus and the within-domain of a gloss-to-text corpus.
Ranked #2 on
Sign Language Translation
on CSL-Daily
Debiased Learning from Naturally Imbalanced Pseudo-Labels
Our key insight is that pseudo-labels are naturally imbalanced due to intrinsic data similarity, even when a model is trained on balanced source data and evaluated on balanced target data.
Ranked #1 on
Few-Shot Image Classification
on ImageNet - 0-Shot
(using extra training data)
Towards Tokenized Human Dynamics Representation
For human action understanding, a popular research direction is to analyze short video clips with unambiguous semantic content, such as jumping and drinking.
One-Shot Generative Domain Adaptation
We then equip the well-learned discriminator backbone with an attribute classifier to ensure that the generator captures the appropriate characters from the reference.
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
Our model starts with two separate pathways: an appearance pathway that outputs feature-based region segmentation for a single image, and a motion pathway that outputs motion features for a pair of images.
Ranked #7 on
Unsupervised Object Segmentation
on FBMS-59
Self-supervised Discovery of Human Actons from Long Kinematic Videos
However, methods for understanding short semantic actions cannot be directly translated to long kinematic sequences such as dancing, where it becomes challenging even to semantically label the human movements.
Aligning Pretraining for Detection via Object-Level Contrastive Learning
Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning.
Instance Localization for Self-supervised Detection Pretraining
The pretext task is to predict the instance category given the composited images as well as the foreground bounding boxes.
Consistent Instance Classification for Unsupervised Representation Learning
The proposed consistent instance classification (ConIC) approach simultaneously optimizes the classification loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance.
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods.
Ranked #2 on
3D Semantic Segmentation
on PartNet
3D Point Cloud Linear Classification
3D Semantic Segmentation
What makes instance discrimination good for transfer learning?
Contrastive visual pretraining based on the instance discrimination pretext task has made significant progress.
A Transductive Approach for Video Object Segmentation
Semi-supervised video object segmentation aims to separate a target object from a video sequence, given the mask in the first frame.
Distilling Localization for Self-Supervised Representation Learning
To address this problem, we propose a data-driven approach for learning invariance to backgrounds.
Deep Metric Transfer for Label Propagation with Limited Annotated Data
In this paper, we propose a generic framework that utilizes unlabeled data to aid generalization for all three tasks.
Improving Generalization via Scalable Neighborhood Component Analysis
Current major approaches to visual recognition follow an end-to-end formulation that classifies an input image into one of the pre-determined set of semantic categories.
Unsupervised Feature Learning via Non-Parametric Instance Discrimination
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so.
Ranked #40 on
Semi-Supervised Image Classification
on ImageNet - 1% labeled data
(Top 5 Accuracy metric)
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so.
Ranked #13 on
Contrastive Learning
on imagenet-1k
Temporal Action Detection with Structured Segment Networks
Detecting actions in untrimmed videos is an important yet challenging task.
Ranked #6 on
Action Recognition
on THUMOS’14
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power.
Adjustable Bounded Rectifiers: Towards Deep Binary Representations
This suggests that the semantic structure of a neural network may be manifested through a guided binarization process.
3D ShapeNets: A Deep Representation for Volumetric Shapes
Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.
Ranked #35 on
3D Point Cloud Classification
on ModelNet40
(Mean Accuracy metric)
