228 citations · 1.5k across the 123 of their papers we have counts for
15 papers · 1 filter
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
Indranil Sur, Zachary Daniels, Abrar Rahman +16
As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adap…
Structure-Encoding Auxiliary Tasks for Improved Visual Representation in Vision-and-Language Navigation
Chia-Wen Kuo, Chih-Yao Ma, Judy Hoffman +1
In Vision-and-Language Navigation (VLN), researchers typically take an image encoder pre-trained on ImageNet without fine-tuning on the environments that the agent will be trained…
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning
James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta +6
Computer vision models suffer from a phenomenon known as catastrophic forgetting when learning novel concepts from continuously shifting training data. Typical solutions for this c…
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning
James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle +7
Recently, large-scale pre-trained Vision-and-Language (VL) foundation models have demonstrated remarkable capabilities in many zero-shot downstream tasks, achieving competitive res…
On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition
Farrukh Rahman, Ömer Mubarek, Zsolt Kira
Recently vision transformers have been shown to be competitive with convolution-based methods (CNNs) broadly across multiple vision tasks. The less restrictive inductive bias of tr…
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Yen-Cheng Liu, Chih-Yao Ma, Junjiao Tian +2
Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a standard method in machine learning. Recently, parameter-efficient fine-tuning methods show…
FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularization
Junjiao Tian, James Seale Smith, Zsolt Kira
Federated Learning (FL) seeks to distribute model training across local clients without collecting data in a centralized data-center, hence removing data-privacy concerns. A major…
Open-Set Semi-Supervised Object Detection
Yen-Cheng Liu, Chih-Yao Ma, Xiaoliang Dai +4
Recent developments for Semi-Supervised Object Detection (SSOD) have shown the promise of leveraging unlabeled data to improve an object detector. However, thus far these methods h…
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
Muhammad Zubair Irshad, Sergey Zakharov, Rares Ambrus +3
Our method studies the complex task of object-centric 3D understanding from a single RGB-D observation. As it is an ill-posed problem, existing methods suffer from low performance…
Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors
Yen-Cheng Liu, Chih-Yao Ma, Zsolt Kira
With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabele…
Lifelong Wandering: A realistic few-shot online continual learning setting
Mayank Lunayach, James Smith, Zsolt Kira
Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes. While prior work in this setting has achiev…
Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning
Chia-Wen Kuo, Zsolt Kira
Significant progress has been made on visual captioning, largely relying on pre-trained features and later fixed object detectors that serve as rich inputs to auto-regressive model…
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients
Yen-Chang Hsu, James Smith, Yilin Shen +2
Knowledge distillation (KD) is a substantial strategy for transferring learned knowledge from one neural network model to another. A vast number of methods have been developed for…
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation
Muhammad Zubair Irshad, Thomas Kollar, Michael Laskey +2
This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation. In contrast to instance-level…
A Closer Look at Rehearsal-Free Continual Learning
James Seale Smith, Junjiao Tian, Shaunak Halbe +2
Continual learning is a setting where machine learning models learn novel concepts from continuously shifting training data, while simultaneously avoiding degradation of knowledge…