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Found 16 papers, 3 papers with code
What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction
The benchmark requires vision-language models to recognize complex human actions, identify possible mistakes, and provide appropriate feedback in real-time.
Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving
Large Language Models (LLMs) have achieved tremendous progress, yet they still often struggle with challenging reasoning problems.
Painter: Teaching Auto-regressive Language Models to Draw Sketches
In this work, we apply LLMs to image generation tasks by directly generating the virtual brush strokes to paint an image.
Look, Remember and Reason: Grounded reasoning in videos with language models
Multi-modal language models (LM) have recently shown promising performance in high-level reasoning tasks on videos.
Differentiable bit-rate estimation for neural-based video codec enhancement
Neural networks (NN) can improve standard video compression by pre- and post-processing the encoded video.
Optimized learned entropy coding parameters for practical neural-based image and video compression
Neural-based image and video codecs are significantly more power-efficient when weights and activations are quantized to low-precision integers.
Boosting neural video codecs by exploiting hierarchical redundancy
In video compression, coding efficiency is improved by reusing pixels from previously decoded frames via motion and residual compensation.
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mobile hardware.
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
We introduce a video compression algorithm based on instance-adaptive learning.
A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space
Most of the existing deep learning based end-to-end video coding (DLEC) architectures are designed specifically for RGB color format, yet the video coding standards, including H. 264/AVC, H. 265/HEVC and H. 266/VVC developed over past few decades, have been designed primarily for YUV 4:2:0 format, where the chrominance (U and V) components are subsampled to achieve superior compression performances considering the human visual system.
Extending Neural P-frame Codecs for B-frame Coding
Our results show that using the proposed method with an existing P-frame codec can lead to 28. 5%saving in bit-rate on the UVG dataset compared to the P-frame codec while generating the same video quality.
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning
We train two classes of neural networks, a fully-convolutional network and an auto-regressive network, and evaluate each as a post-quantization step designed to refine cheap quantization schemes such as scalar quantization (SQ).
Automatic exposure selection and fusion for high-dynamic-range photography via smartphones
In this paper, a method for automatically selecting the exposure settings of such images is introduced based on the camera characteristic function.
Feedback Recurrent Autoencoder for Video Compression
Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
