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Found 14 papers, 3 papers with code
Semantics Disentanglement and Composition for Versatile Codec toward both Human-eye Perception and Machine Vision Task
While learned image compression methods have achieved impressive results in either human visual perception or machine vision tasks, they are often specialized only for one domain.
Tell Codec What Worth Compressing: Semantically Disentangled Image Coding for Machine with LMMs
We present a new image compression paradigm to achieve ``intelligently coding for machine'' by cleverly leveraging the common sense of Large Multimodal Models (LMMs).
Rate-Distortion-Cognition Controllable Versatile Neural Image Compression
Recently, the field of Image Coding for Machines (ICM) has garnered heightened interest and significant advances thanks to the rapid progress of learning-based techniques for image compression and analysis.
Closed-Loop Unsupervised Representation Disentanglement with $β$-VAE Distillation and Diffusion Probabilistic Feedback
Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks.
One at a Time: Progressive Multi-step Volumetric Probability Learning for Reliable 3D Scene Perception
Numerous studies have investigated the pivotal role of reliable 3D volume representation in scene perception tasks, such as multi-view stereo (MVS) and semantic scene completion (SSC).
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model
In this paper, we present a novel framework (EMoG) to tackle the above challenges with denoising diffusion models: 1) To alleviate the one-to-many problem, we incorporate emotion clues to guide the generation process, making the generation much easier; 2) To model joint correlation, we propose to decompose the difficult gesture generation into two sub-problems: joint correlation modeling and temporal dynamics modeling.
Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts
For ICM, developing a unified codec to reduce information redundancy while empowering the compressed features to support various vision tasks is very important, which inevitably faces two core challenges: 1) How should the compression strategy be adjusted based on the downstream tasks?
Inpaint Anything: Segment Anything Meets Image Inpainting
We are also very willing to help everyone share and promote new projects based on our Inpaint Anything (IA).
Learned Image Compression with Mixed Transformer-CNN Architectures
Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages.
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Image Compression
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Multistage Spatial Context Models for Learned Image Compression
The proposed method features a comparable decoding speed to Checkerboard while reaching the RD performance of Autoregressive and even also outperforming Autoregressive.
Semantic Segmentation in Learned Compressed Domain
Most machine vision tasks (e. g., semantic segmentation) are based on images encoded and decoded by image compression algorithms (e. g., JPEG).
Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules
Lossless image compression is an essential research field in image compression.
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module
Learned Image Compression (LIC) gradually became more and more famous in these years.
A Multi-stream Convolutional Neural Network for Micro-expression Recognition Using Optical Flow and EVM
On the other hand, some methods based on deep learning also cannot get high accuracy due to problems such as the imbalance of databases.
Micro Expression Recognition
Micro-Expression Recognition
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