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Found 16 papers, 8 papers with code
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.
Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences
The factors examined are gender differences, syntactic patterns, and bipolar recognition performance.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
MaskCRT: Masked Conditional Residual Transformer for Learned Video Compression
Conditional coding has lately emerged as the mainstream approach to learned video compression.
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 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.
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.
