Search Results for author:
Found 33 papers, 12 papers with code
Is Self-Supervised Learning More Robust Than Supervised Learning?
Our insight has implications in improving the downstream robustness of supervised learning.
Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields
Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception.
Long-Tailed Recognition via Weight Balancing
In contrast, weight decay penalizes larger weights more heavily and so learns small balanced weights; the MaxNorm constraint encourages growing small weights within a norm ball but caps all the weights by the radius.
Ranked #1 on
Long-tail Learning
on CIFAR-100-LT (ρ=50)
Discovering Objects that Can Move
Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects.
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence
We hypothesize that the diversity coefficient of the few-shot learning benchmark is predictive of whether meta-learning solutions will succeed or not.
Does MAML Only Work via Feature Re-use? A Data Centric Perspective
Recent work has suggested that a good embedding is all we need to solve many few-shot learning benchmarks.
Embracing Single Stride 3D Object Detector with Sparse Transformer
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Ranked #1 on
3D Object Detection
on waymo pedestrian
On the Importance of Firth Bias Reduction in Few-Shot Classification
In this work, we demonstrate the effectiveness of Firth bias reduction in few-shot classification.
Generative Modeling for Multitask Visual Learning
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images.
On the Importance of Distractors for Few-Shot Classification
An effective approach to few-shot classification involves a prior model trained on a large-sample base domain, which is then finetuned over the novel few-shot task to yield generalizable representations.
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
We leverage the pixel-level L2 loss and the pixel contrastive loss for the two purposes respectively.
Generative Modeling for Multi-task Visual Learning
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images.
Hallucination Improves Few-Shot Object Detection
One critical factor in improving few-shot detection is to address the lack of variation in training data.
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level.
DAP: Detection-Aware Pre-training with Weak Supervision
This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e. g., ImageNet) for pre-training, but is specifically tailored to benefit object detection tasks.
Learning To Hallucinate Examples From Extrinsic and Intrinsic Supervision
Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks.
Cooperating RPN's Improve Few-Shot Object Detection
Learning to detect an object in an image from very few training examples - few-shot object detection - is challenging, because the classifier that sees proposal boxes has very little training data.
Few-Shot Learning with Intra-Class Knowledge Transfer
Recent works have proposed to solve this task by augmenting the training data of the few-shot classes using generative models with the few-shot training samples as the seeds.
Alpha Net: Adaptation with Composition in Classifier Space
Motivated by the human ability to solve this task, models have been developed that transfer knowledge from classes with many examples to learn classes with few examples.
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints.
Towards Streaming Perception
While past work has studied the algorithmic trade-off between latency and accuracy, there has not been a clear metric to compare different methods along the Pareto optimal latency-accuracy curve.
Ranked #2 on
Real-Time Object Detection
on Argoverse-HD (Detection-Only, Val)
(using extra training data)
Unlocking the Full Potential of Small Data with Diverse Supervision
Indeed, even the majority of few-shot learning methods rely on a large set of "base classes" for pretraining.
Meta-Learning to Detect Rare Objects
Few-shot learning, i. e., learning novel concepts from few examples, is fundamental to practical visual recognition systems.
Ranked #14 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Meta-Learning by Hallucinating Useful Examples
Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks, which aim to learn novel concepts from very few examples.
Growing a Brain: Fine-Tuning by Increasing Model Capacity
One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset.
Image Deformation Meta-Networks for One-Shot Learning
Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information.
Learning Compositional Representations for Few-Shot Recognition
One of the key limitations of modern deep learning approaches lies in the amount of data required to train them.
Few-Shot Human Motion Prediction via Meta-Learning
This paper addresses the problem of few-shot human motion prediction, in the spirit of the recent progress on few-shot learning and meta-learning.
Adversarial Geometry-Aware Human Motion Prediction
We explore an approach to forecasting human motion in a few milliseconds given an input 3D skeleton sequence based on a recurrent encoder-decoder framework.
Low-Shot Learning from Imaginary Data
Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views.
Learning to Model the Tail
We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to the data-poor classes in the tail.
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs
Inspired by the transferability properties of CNNs, we introduce an additional unsupervised meta-training stage that exposes multiple top layer units to a large amount of unlabeled real-world images.
Model Recommendation: Generating Object Detectors From Few Samples
In this paper, we explore an approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples.
