Trending Research
Pen and Paper Exercises in Machine Learning
This is a collection of (mostly) pen-and-paper exercises in machine learning.
Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation
Vegetebird/StridedTransformer-Pose3D
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The modified VTE is termed as Strided Transformer Encoder (STE), which is built upon the outputs of VTE.
Ranked #1 on
3D Human Pose Estimation
on HumanEva-I
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge.
Ranked #1 on
Text-to-Image Generation
on COCO
Multi-Graph Fusion Networks for Urban Region Embedding
wushangbin/mgfn
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Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
tjiiv-cprg/epro-pnp
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CVPR 2022
The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution.
Ranked #4 on
6D Pose Estimation using RGB
on LineMOD
Ivy: Templated Deep Learning for Inter-Framework Portability
ivy-dl/ivy
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We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks.
HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction
Reconstructing 3D objects is an important computer vision task that has wide application in AR/VR.
OmniXAI: A Library for Explainable AI
We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and interpreting the decisions made by machine learning (ML) in practice.
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the positive instances, substantially improving the robustness of the MIL approach to the negative instances from abnormal videos.
Anomaly Detection In Surveillance Videos
Contrastive Learning
+1
Matryoshka Representations for Adaptive Deployment
raivnlab/mrl
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The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations.
