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Found 10 papers, 7 papers with code
Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation
Vanilla text-to-image diffusion models struggle with generating accurate human images, commonly resulting in imperfect anatomies such as unnatural postures or disproportionate limbs. Existing methods address this issue mostly by fine-tuning the model with extra images or adding additional controls -- human-centric priors such as pose or depth maps -- during the image generation phase.
OVO: Open-Vocabulary Occupancy
Semantic occupancy prediction aims to infer dense geometry and semantics of surroundings for an autonomous agent to operate safely in the 3D environment.
Entroformer: A Transformer-based Entropy Model for Learned Image Compression
One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules.
GiraffeDet: A Heavy-Neck Paradigm for Object Detection
This heavy-backbone design paradigm is mostly due to the historical legacy when transferring image recognition models to object detection rather than an end-to-end optimized design for object detection.
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS).
Ranked #88 on
Object Detection
on COCO minival
ZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search
Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS).
Interpolation variable rate image compression
Compression standards have been used to reduce the cost of image storage and transmission for decades.
Spatiotemporal Entropy Model is All You Need for Learned Video Compression
The framework of dominant learned video compression methods is usually composed of motion prediction modules as well as motion vector and residual image compression modules, suffering from its complex structure and error propagation problem.
Learning Accurate Entropy Model with Global Reference for Image Compression
In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate.
Learning to Rank Proposals for Object Detection
Non-Maximum Suppression (NMS) is an essential step of modern object detection models for removing duplicated candidates.
