Trending Research
Wavelet Diffusion Models are fast and scalable Image Generators
Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances.
Ranked #1 on
Image Generation
on CelebA-HQ 512x512
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
KU-CVLAB/CAT-Seg
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However, the problem of transferring these capabilities learned from image-level supervision to the pixel-level task of segmentation and addressing arbitrary unseen categories at inference makes this task challenging.
Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
In this paper, we propose a long-sequence modeling framework, named StreamPETR, for multi-view 3D object detection.
Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
anosorae/irra
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To alleviate these issues, we present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework that learns relations between local visual-textual tokens and enhances global image-text matching without requiring additional prior supervision.
Ranked #1 on
Text based Person Retrieval
on RSTPReid
(using extra training data)
Nerfstudio: A Modular Framework for Neural Radiance Field Development
nerfstudio-project/nerfstudio
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Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more.
KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation
Relative positional embeddings (RPE) have received considerable attention since RPEs effectively model the relative distance among tokens and enable length extrapolation.
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
To democratize this, we train and release a family of large language models up to 16. 1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER.
Ranked #1 on
Program Synthesis
on HumanEval
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
idea-research/groundingdino
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To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion.
Ranked #1 on
Zero-Shot Object Detection
on MSCOCO
Memorizing Transformers
lucidrains/memorizing-transformers-pytorch
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ICLR 2022
Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights.
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
adap/flower
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ICLR 2021
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.
