4,693 research outputs found
Universal Deep Image Compression via Content-Adaptive Optimization with Adapters
Deep image compression performs better than conventional codecs, such as
JPEG, on natural images. However, deep image compression is learning-based and
encounters a problem: the compression performance deteriorates significantly
for out-of-domain images. In this study, we highlight this problem and address
a novel task: universal deep image compression. This task aims to compress
images belonging to arbitrary domains, such as natural images, line drawings,
and comics. To address this problem, we propose a content-adaptive optimization
framework; this framework uses a pre-trained compression model and adapts the
model to a target image during compression. Adapters are inserted into the
decoder of the model. For each input image, our framework optimizes the latent
representation extracted by the encoder and the adapter parameters in terms of
rate-distortion. The adapter parameters are additionally transmitted per image.
For the experiments, a benchmark dataset containing uncompressed images of four
domains (natural images, line drawings, comics, and vector arts) is constructed
and the proposed universal deep compression is evaluated. Finally, the proposed
model is compared with non-adaptive and existing adaptive compression models.
The comparison reveals that the proposed model outperforms these. The code and
dataset are publicly available at https://github.com/kktsubota/universal-dic.Comment: Accepted at the IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV) 202
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Digital Interaction and Machine Intelligence
This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction
Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction
Keyphrase extraction (KPE) is an important task in Natural Language
Processing for many scenarios, which aims to extract keyphrases that are
present in a given document. Many existing supervised methods treat KPE as
sequential labeling, span-level classification, or generative tasks. However,
these methods lack the ability to utilize keyphrase information, which may
result in biased results. In this study, we propose Diff-KPE, which leverages
the supervised Variational Information Bottleneck (VIB) to guide the text
diffusion process for generating enhanced keyphrase representations. Diff-KPE
first generates the desired keyphrase embeddings conditioned on the entire
document and then injects the generated keyphrase embeddings into each phrase
representation. A ranking network and VIB are then optimized together with rank
loss and classification loss, respectively. This design of Diff-KPE allows us
to rank each candidate phrase by utilizing both the information of keyphrases
and the document. Experiments show that Diff-KPE outperforms existing KPE
methods on a large open domain keyphrase extraction benchmark, OpenKP, and a
scientific domain dataset, KP20K.Comment: 10 pages, 2 figure
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
The latest advancements in neural image compression show great potential in
surpassing the rate-distortion performance of conventional standard codecs.
Nevertheless, there exists an indelible domain gap between the datasets
utilized for training (i.e., natural images) and those utilized for inference
(e.g., artistic images). Our proposal involves a low-rank adaptation approach
aimed at addressing the rate-distortion drop observed in out-of-domain
datasets. Specifically, we perform low-rank matrix decomposition to update
certain adaptation parameters of the client's decoder. These updated
parameters, along with image latents, are encoded into a bitstream and
transmitted to the decoder in practical scenarios. Due to the low-rank
constraint imposed on the adaptation parameters, the resulting bit rate
overhead is small. Furthermore, the bit rate allocation of low-rank adaptation
is \emph{non-trivial}, considering the diverse inputs require varying
adaptation bitstreams. We thus introduce a dynamic gating network on top of the
low-rank adaptation method, in order to decide which decoder layer should
employ adaptation. The dynamic adaptation network is optimized end-to-end using
rate-distortion loss. Our proposed method exhibits universality across diverse
image datasets. Extensive results demonstrate that this paradigm significantly
mitigates the domain gap, surpassing non-adaptive methods with an average
BD-rate improvement of approximately across out-of-domain images.
Furthermore, it outperforms the most advanced instance adaptive methods by
roughly BD-rate. Ablation studies confirm our method's ability to
universally enhance various image compression architectures.Comment: Accepted by ACM MM 2023, 13 pages, 12 figure
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