Trending Research
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56. 8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100.
Ranked #20 on
Object Detection
on COCO test-dev
Pen and Paper Exercises in Machine Learning
This is a collection of (mostly) pen-and-paper exercises in machine learning.
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
kwea123/ngp_pl
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Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate.
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation
We also use a point refinement module via 3D sparse convolution to fuse the information from both LiDAR range image and point cloud representations and reduce the artifacts on the borders of the objects.
AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture
Then given a monocular RGB video of this subject, our method integrates information from both the image observation and the avatar prior, and accordingly recon-structs high-fidelity 3D textured models with dynamic details regardless of the visibility.
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
To address the limitations, we propose "CodeRL", a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning (RL).
DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech
We sampled, modified, and recorded 2, 541 dialogues from the open-domain dialogue dataset DailyDialog which are adequately long to represent context of each dialogue.
Text2Human: Text-Driven Controllable Human Image Generation
In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation.
Back to MLP: A Simple Baseline for Human Motion Prediction
dulucas/simlpe
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This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences.
Disentangling Random and Cyclic Effects in Time-Lapse Sequences
harskish/tlgan
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We introduce the problem of disentangling time-lapse sequences in a way that allows separate, after-the-fact control of overall trends, cyclic effects, and random effects in the images, and describe a technique based on data-driven generative models that achieves this goal.
