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Found 28 papers, 9 papers with code
HaVTR: Improving Video-Text Retrieval Through Augmentation Using Large Foundation Models
While recent progress in video-text retrieval has been driven by the exploration of powerful model architectures and training strategies, the representation learning ability of video-text retrieval models is still limited due to low-quality and scarce training data annotations.
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model
Specifically, we inject the priors into the denoising model to improve the utilization of reference information in unchanged areas and regulate the reconstruction of semantically relevant content in changed areas.
A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond
Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains.
DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing Data
Nighttime light (NTL) remote sensing observation serves as a unique proxy for quantitatively assessing progress toward meeting a series of Sustainable Development Goals (SDGs), such as poverty estimation, urban sustainable development, and carbon emission.
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping
However, when applied to the infrared destriping task, it becomes challenging for the vanilla auxiliary generator to consistently produce vertical noise under unsupervised constraints.
Optimizing for ROC Curves on Class-Imbalanced Data by Training over a Family of Loss Functions
Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem.
KS-Lottery: Finding Certified Lottery Tickets for Multilingual Language Models
We further theoretically prove that KS-Lottery can find the certified winning tickets in the embedding layer, fine-tuning on the found parameters is guaranteed to perform as well as full fine-tuning.
SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection
In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full scales.
ARCNet: An Asymmetric Residual Wavelet Column Correction Network for Infrared Image Destriping
Our neural model leverages a novel downsampler, residual haar discrete wavelet transform (RHDWT), stripe directional prior knowledge and data-driven learning to induce a model with enriched feature representation of stripe noise and background.
How Multilingual is Multilingual LLM?
Large Language Models (LLMs), trained predominantly on extensive English data, often exhibit limitations when applied to other languages.
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language(e. g., chemical molecular formula).
A review of individual tree crown detection and delineation from optical remote sensing images
); (2) traditional machine learning methods (such as random forest, decision tree, etc.
UFD-PRiME: Unsupervised Joint Learning of Optical Flow and Stereo Depth through Pixel-Level Rigid Motion Estimation
A second network, trained with optical flow from the first as pseudo-labels, takes disparities from the first network, estimates 3D rigid motion at every pixel, and reconstructs optical flow again.
SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving
We show that additional information such as semantics and domain knowledge can help better constrain this problem.
Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation
Moreover, we propose the Class Center Contrast method to jointly utilize the labeled and unlabeled data.
Unsupervised Flow Refinement near Motion Boundaries
Unsupervised optical flow estimators based on deep learning have attracted increasing attention due to the cost and difficulty of annotating for ground truth.
Cross-Attention Transformer for Video Interpolation
We propose TAIN (Transformers and Attention for video INterpolation), a residual neural network for video interpolation, which aims to interpolate an intermediate frame given two consecutive image frames around it.
Optical Flow Training under Limited Label Budget via Active Learning
Supervised training of optical flow predictors generally yields better accuracy than unsupervised training.
RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments
Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency.
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing
In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and globally optimal spectrum utilization is achieved through distributed decision-making.
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands
In this paper, a noise attention method is proposed for unsupervised spectrum anomaly detection in unauthorized bands.
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Solving this problem using a learning-to-learn approach -- using meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates -- was recently shown to be effective.
Spatially Constrained Spectral Clustering Algorithms for Region Delineation
To address these two challenges, first, we develop a spatially constrained spectral clustering framework for region delineation that incorporates the tradeoff between region homogeneity and spatial contiguity.
Learning Continuous User Representations through Hybrid Filtering with doc2vec
In order to maximize the predictive performance of our look-alike modeling algorithms, we propose two novel hybrid filtering techniques that utilize the recent neural probabilistic language model algorithm doc2vec.
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads.
Computer Science and Game Theory
Real-Time Bidding Benchmarking with iPinYou Dataset
This dataset directly supports the experiments of some important research problems such as bid optimisation and CTR estimation.
Computer Science and Game Theory Computers and Society
A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising
From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB.
Computer Science and Game Theory
Optimisation for job scheduling at automated container terminals using genetic algorithm
The GA-based approach has been implemented in the terminal scheduling system and the live testing results show that the GA-based approach can reduce the overall time-related cost of container transfers at the automated container terminal.
