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Found 17 papers, 8 papers with code
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification
In this work, we propose a simple yet effective unified model, coined ConTextING, with a joint training mechanism to learn from both document embeddings and contextual word interactions simultaneously.
ComNeck: Bridging Compressed Image Latents and Multimodal LLMs via Universal Transform-Neck
MLLMs have extended the success of large language models to modalities (e. g. images) beyond text, but their billion scale hinders deployment on resource-constrained end devices.
Transformer-based Learned Image Compression for Joint Decoding and Denoising
Instead of training separate decoders for these tasks, we incorporate two add-on modules to adapt a pre-trained image decoder from performing the standard image reconstruction to joint decoding and denoising.
LiDAR Depth Map Guided Image Compression Model
In this paper, we introduce a novel direction that harnesses LiDAR depth maps to enhance the compression of the corresponding RGB camera images.
Transformer-based Image Compression with Variable Image Quality Objectives
This paper presents a Transformer-based image compression system that allows for a variable image quality objective according to the user's preference.
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution
We motivate the use of forward motion from the perspective of learning individual motion trajectories, as opposed to learning a mixture of motion trajectories with backward motion.
TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception
This work aims for transferring a Transformer-based image compression codec from human perception to machine perception without fine-tuning the codec.
Transformer-based Variable-rate Image Compression with Region-of-interest Control
Our prompt generation networks generate content-adaptive tokens according to the input image, an ROI mask, and a rate parameter.
Content-Adaptive Motion Rate Adaption for Learned Video Compression
This paper introduces an online motion rate adaptation scheme for learned video compression, with the aim of achieving content-adaptive coding on individual test sequences to mitigate the domain gap between training and test data.
B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows
Our B*-frames allow greater flexibility in specifying the group-of-pictures (GOP) structure by reusing the B-frame codec to mimic P-frame coding, without the need for an additional, separate P-frame codec.
Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling
Our Long Short-Term Memory Video Rescaling Network (LSTM-VRN) leverages temporal information in the low-resolution video to form an explicit prediction of the missing high-frequency information for upscaling.
Class-incremental Learning with Rectified Feature-Graph Preservation
In this paper, we address the problem of distillation-based class-incremental learning with a single head.
Generation of sub-MHz and spectrally-bright biphotons from hot atomic vapors with a phase mismatch-free scheme
The generation rate per linewidth of the 610-kHz biphoton source is 1, 500 pairs/(s$\cdot$MHz), which is the best result of all the sub-MHz biphoton sources in the literature.
Quantum Physics
EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation
By the nature of the framework of BERT, a two-sentence structure, we adapt BERT to continues dialogue emotion prediction tasks, which rely heavily on the sentence-level context-aware understanding.
Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences
The factors examined are gender differences, syntactic patterns, and bipolar recognition performance.
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases.
