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Found 13 papers, 10 papers with code
MaskBit: Embedding-free Image Generation via Bit Tokens
Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models.
Ranked #7 on
Image Reconstruction
on ImageNet
MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation
Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e. g., synthetic data, and an unlabeled target domain.
An Image is Worth 32 Tokens for Reconstruction and Generation
At ImageNet 512 x 512 benchmark, TiTok not only outperforms state-of-the-art diffusion model DiT-XL/2 (gFID 2. 74 vs. 3. 04), but also reduces the image tokens by 64x, leading to 410x faster generation process.
Ranked #8 on
Image Reconstruction
on ImageNet
The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset
We posit that certain domain applications, such as anti-money laundering (AML), are inherently subgraph problems and mainstream graph techniques have been operating at a suboptimal level of abstraction.
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes.
DeepLab2: A TensorFlow Library for Deep Labeling
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.
4D Panoptic LiDAR Segmentation
In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.
Ranked #7 on
4D Panoptic Segmentation
on SemanticKITTI
STEP: Segmenting and Tracking Every Pixel
The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation.
Single-Shot Panoptic Segmentation
We present a novel end-to-end single-shot method that segments countable object instances (things) as well as background regions (stuff) into a non-overlapping panoptic segmentation at almost video frame rate.
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency.
Visual Person Understanding through Multi-Task and Multi-Dataset Learning
We address the problem of learning a single model for person re-identification, attribute classification, body part segmentation, and pose estimation.
4D Generic Video Object Proposals
Many high-level video understanding methods require input in the form of object proposals.
Scalable Graph Learning for Anti-Money Laundering: A First Look
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150, 000 people since 2006, upwards of 700, 000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people.
