Search Results for author:
Found 16 papers, 5 papers with code
Return of Unconditional Generation: A Self-supervised Representation Generation Method
This gap can be attributed to the lack of semantic information provided by labels.
Ranked #1 on
Unconditional Image Generation
on ImageNet 256x256
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
We demonstrate image generation and captioning performance on par with state-of-the-art text-to-image and image-to-text models with orders of magnitude fewer (only 3M) paired image-text data.
Reparo: Loss-Resilient Generative Codec for Video Conferencing
Packet loss during video conferencing often leads to poor quality and video freezing.
MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis
In this work, we propose MAsked Generative Encoder (MAGE), the first framework to unify SOTA image generation and self-supervised representation learning.
Ranked #2 on
Unconditional Image Generation
on ImageNet 256x256
Unsupervised Learning for Human Sensing Using Radio Signals
Thus, in this paper, we explore the feasibility of adapting RGB-based unsupervised representation learning to RF signals.
Targeted Supervised Contrastive Learning for Long-Tailed Recognition
This forces all classes, including minority classes, to maintain a uniform distribution in the feature space, improves class boundaries, and provides better generalization even in the presence of long-tail data.
Ranked #22 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Addressing Feature Suppression in Unsupervised Visual Representations
However, contrastive learning is susceptible to feature suppression, i. e., it may discard important information relevant to the task of interest, and learn irrelevant features.
In-Home Daily-Life Captioning Using Radio Signals
This paper aims to caption daily life --i. e., to create a textual description of people's activities and interactions with objects in their homes.
Learning Longterm Representations for Person Re-Identification Using Radio Signals
RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape.
Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Understanding people's actions and interactions typically depends on seeing them.
Ranked #1 on
RF-based Pose Estimation
on RF-MMD
Few Sample Knowledge Distillation for Efficient Network Compression
Deep neural network compression techniques such as pruning and weight tensor decomposition usually require fine-tuning to recover the prediction accuracy when the compression ratio is high.
Knowledge Distillation from Few Samples
Taking the assumption that both "teacher" and "student" have the same feature map sizes at each corresponding block, we add a $1\times 1$ conv-layer at the end of each block in the student-net, and align the block-level outputs between "teacher" and "student" by estimating the parameters of the added layer with limited samples.
RF-based 3D skeletons
It maintains this accuracy even in the presence of multiple people, and in new environments that it has not seen in the training set.
Through-Wall Human Pose Estimation Using Radio Signals
Yet, unlike vision-based pose estimation, the radio-based system can estimate 2D poses through walls despite never trained on such scenarios.
Multi-Scale Dense Networks for Resource Efficient Image Classification
In this paper we investigate image classification with computational resource limits at test time.
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$, introduced by Zilles et al. (2011), is a combinatorial complexity measure characterized by the worst-case number of examples necessary to identify a concept in $\mathcal C$ according to the recursive teaching model.
