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Found 14 papers, 8 papers with code
Learning to Classify New Foods Incrementally Via Compressed Exemplars
Therefore, food image classification systems should adapt to and manage data that continuously evolves.
Flexible Variable-Rate Image Feature Compression for Edge-Cloud Systems
By compressing different intermediate features of a pre-trained vision task model, the proposed method can scale the encoding complexity without changing the overall size of the model.
Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs
Recent studies reveal a significant theoretical link between variational autoencoders (VAEs) and rate-distortion theory, notably in utilizing VAEs to estimate the theoretical upper bound of the information rate-distortion function of images.
Probing Image Compression For Class-Incremental Learning
To this end, we introduce a new framework to incorporate image compression for continual ML including a pre-processing data compression step and an efficient compression rate/algorithm selection method.
Towards Backward-Compatible Continual Learning of Image Compression
This paper explores the possibility of extending the capability of pre-trained neural image compressors (e. g., adapting to new data or target bitrates) without breaking backward compatibility, the ability to decode bitstreams encoded by the original model.
Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding
While convolution and self-attention are extensively used in learned image compression (LIC) for transform coding, this paper proposes an alternative called Contextual Clustering based LIC (CLIC) which primarily relies on clustering operations and local attention for correlation characterization and compact representation of an image.
Deep Hierarchical Video Compression
Recently, probabilistic predictive coding that directly models the conditional distribution of latent features across successive frames for temporal redundancy removal has yielded promising results.
An Improved Upper Bound on the Rate-Distortion Function of Images
Recent work has shown that Variational Autoencoders (VAEs) can be used to upper-bound the information rate-distortion (R-D) function of images, i. e., the fundamental limit of lossy image compression.
QARV: Quantization-Aware ResNet VAE for Lossy Image Compression
This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications.
Efficient Feature Compression for Edge-Cloud Systems
Optimizing computation in an edge-cloud system is an important yet challenging problem.
Lossy Image Compression with Quantized Hierarchical VAEs
Recent research has shown a strong theoretical connection between variational autoencoders (VAEs) and the rate-distortion theory.
Opening the Black Box of Learned Image Coders
End-to-end learned lossy image coders (LICs), as opposed to hand-crafted image codecs, have shown increasing superiority in terms of the rate-distortion performance.
Weyl invariant Jacobi forms along Higgsing trees
Our computations are made feasible by the fact that symmetry enhancements of the gauge theory which are manifest on the massless spectrum are inherited by the entire tower of BPS particles.
High Energy Physics - Theory Algebraic Geometry
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images
Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity.
