Tetromino Pixel

"Tetromino Pixel," a term encompassing various research directions, broadly focuses on leveraging pixel-level information from images and videos to achieve higher-level tasks. Current research emphasizes using deep learning models, including transformers, U-Nets, and diffusion models, to process visual data and integrate it with other modalities like text and 3D point clouds for applications such as image captioning, object detection, 3D reconstruction, and robotic control. This work is significant for advancing multimodal AI, improving the efficiency and interpretability of computer vision systems, and enabling new capabilities in areas like autonomous navigation and medical image analysis.

Papers

December 21, 2022

Generalized Decoding for Pixel, Image, and Language
Xueyan Zou, Zi-Yi Dou, Jianwei Yang, Zhe Gan, Linjie Li, Chunyuan Li, Xiyang Dai, Harkirat Behl, Jianfeng Wang, Lu Yuan, Nanyun Peng, Lijuan Wang, Yong Jae Lee, Jianfeng Gao
Human Language Pixel Level Tetromino Pixel Accurate Decoding Open Vocabulary Segmentation

December 20, 2022

Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework
Wei Lou, Haofeng Li, Guanbin Li, Xiaoguang Han, Xiang Wan
Segmentation Model Tetromino Pixel Nucleus Segmentation Manual Annotation Nucleus Instance Segmentation

December 15, 2022

CLIPPO: Image-and-Language Understanding from Pixels Only
Michael Tschannen, Basil Mustafa, Neil Houlsby
Contrastive Loss Multimodal Model Tetromino Pixel Multimodal Task Text Contrastive Learning Clipped Stochastic Gradient Descent

December 13, 2022

Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
Zhenyu Wu, Lin Wang, Wei Wang, Qing Xia, Chenglizhao Chen, Aimin Hao, Shuo Li
Weakly Supervised Tetromino Pixel SALient Object Detection Adversarial Trajectory

November 14, 2022

PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
Mu Chen, Zhedong Zheng, Yi Yang, Tat-Seng Chua
Semantic Segmentation Domain Adaptation Tetromino Pixel Image Domain

November 1, 2022

Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions
Alexey Skrynnik, Zoya Volovikova, Marc-Alexandre Côté, Anton Voronov, Artem Zholus, Negar Arabzadeh, Shrestha Mohanty, Milagro Teruel, Ahmed Awadallah, Aleksandr Panov, Mikhail Burtsev, Julia Kiseleva
Language Model Reinforcement Learning LeArning Abstract Natural Language Instruction Tetromino Pixel Sub Goal Pre Trained Reinforcement Learning

October 2, 2022

Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
Yannick Hogewind, Thiago D. Simao, Tal Kachman, Nils Jansen
Safe Reinforcement Learning Tetromino Pixel Observable Markov Decision Process Partial Observability Reward Maximization Safety Critic

September 24, 2022

Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron Courville, Alexandre Lacoste
Reinforcement Learning Tetromino Pixel Reinforcement Learning Benchmark Unsupervised Reinforcement Learning

September 23, 2022

Image Classification using Sequence of Pixels
Gajraj Kuldeep
Image Classification Recurrent Neural Network Long Short Term Memory LSTM Network Tetromino Pixel Sequence of Sequence

July 30, 2022

Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking
Arash Rabbani, Hao Gao, Dirk Husmeier
Tetromino Pixel Cardiac Magnetic Resonance Scene Extrapolation Ventricle Segmentation

July 27, 2022

Adaptive sampling for scanning pixel cameras
Yusuf Duman, Jean-Yves Guillemaut, Simon Hadfield
Semantic Segmentation Adaptive Importance Image Quality Tetromino Pixel Low Power High Speed Imaging

July 26, 2022

PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers
Namgyu Kang, Byeonghyeon Lee, Youngjoon Hong, Seok-Bae Yun, Eunbyung Park
Physic Informed Neural Network Tetromino Pixel PDE Solver Cell Representation Inverse PDE Problem

July 14, 2022

Language Modelling with Pixels
Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, Desmond Elliott
Language Model Pretrained Language Model Tetromino Pixel Text Encoder Language Modelling Code Switched

July 3, 2022

Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
Edoardo Cetin, Philip J. Ball, Steve Roberts, Oya Celiktutan
Policy Reinforcement Learning Tetromino Pixel Convolutional Encoder DeepMind Control Suite Catastrophic Overfitting Policy Deep Reinforcement Learning Training Instability

June 13, 2022

Pixel to Binary Embedding Towards Robustness for CNNs
Ikki Kishida, Hideki Nakayama
Convolutional Neural Network Native Robustness Adversarial Perturbation Tetromino Pixel Binary Embedding

June 10, 2022

Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
Xiang Li, Jinghuan Shang, Srijan Das, Michael S. Ryoo
Reinforcement Learning Self Supervised Learning Tetromino Pixel Online Reinforcement Learning Self Supervised Loss Contrastive Reinforcement Learning

June 8, 2022

Deep Hierarchical Planning from Pixels
Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel
World Model Sparse Reward Tetromino Pixel Hierarchical Reinforcement Learning Latent Intent

June 4, 2022

From Pixels to Objects: Cubic Visual Attention for Visual Question Answering
Jingkuan Song, Pengpeng Zeng, Lianli Gao, Heng Tao Shen
Visual Question Answering Arbitrary Object Tetromino Pixel Channel Attention Visual Question Answering Model 3D Attention

May 23, 2022

Denoising-based image reconstruction from pixels located at non-integer positions
Ján Koloda, Jürgen Seiler, André Kaup
Image Reconstruction Image Processing Tetromino Pixel Motion Compensation Positional Label

April 11, 2022

Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels
Tianxin Tao, Daniele Reda, Michiel van de Panne
Vision Transformer Deep Reinforcement Learning Transformer Architecture Tetromino Pixel Image Based Reinforcement Learning