Search Results for author:
Found 13 papers, 7 papers with code
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Asymmetric self-play for automatic goal discovery in robotic manipulation
We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects.
Behavior Priors for Efficient Reinforcement Learning
In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts.
Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning
We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning.
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL
As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reusable structure in solution space is likely to become increasingly important.
Transfer Learning via Unsupervised Task Discovery for Visual Question Answering
Specifically, we employ linguistic knowledge sources such as structured lexical database (e. g. WordNet) and visual descriptions for unsupervised task discovery, and transfer a learned task conditional visual classifier as an answering unit in a visual question answering model.
Neural Program Synthesis from Diverse Demonstration Videos
To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos.
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance.
Large-Scale Image Retrieval with Attentive Deep Local Features
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature).
Ranked #2 on
Image Retrieval
on Oxf105k
Training Recurrent Answering Units with Joint Loss Minimization for VQA
We propose a novel algorithm for visual question answering based on a recurrent deep neural network, where every module in the network corresponds to a complete answering unit with attention mechanism by itself.
Ranked #2 on
Visual Question Answering (VQA)
on VQA v1 test-std
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on questions.
Image Retrieval with Multi-Modal Query
Parameter Prediction
+2
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations.
Learning Deconvolution Network for Semantic Segmentation
We propose a novel semantic segmentation algorithm by learning a deconvolution network.
Ranked #3 on
Curved Text Detection
on SCUT-CTW1500
