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Found 20 papers, 10 papers with code
To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large Language Models
However, there is still considerable debate on constructing MLLM architectures, particularly regarding the selection of appropriate connectors for perception tasks of varying granularities.
Semantic Communication based on Large Language Model for Underwater Image Transmission
Underwater communication is essential for environmental monitoring, marine biology research, and underwater exploration.
Recurrent Context Compression: Efficiently Expanding the Context Window of LLM
To extend the context length of Transformer-based large language models (LLMs) and improve comprehension capabilities, we often face limitations due to computational resources and bounded memory storage capacity.
Adaptive Rentention & Correction for Continual Learning
A common problem in continual learning is the classification layer's bias towards the most recent task.
A Survey on Video Diffusion Models
However, existing surveys mainly focus on diffusion models in the context of image generation, with few up-to-date reviews on their application in the video domain.
Panoptic Vision-Language Feature Fields
In this paper, we propose to the best of our knowledge the first algorithm for open-vocabulary panoptic segmentation in 3D scenes.
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost.
PromptFusion: Decoupling Stability and Plasticity for Continual Learning
Current research on continual learning mainly focuses on relieving catastrophic forgetting, and most of their success is at the cost of limiting the performance of newly incoming tasks.
Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
Most existing methods for unsupervised domain adaptation (UDA) rely on a shared network to extract domain-invariant features.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Facial Attribute Transformers for Precise and Robust Makeup Transfer
In this paper, we address the problem of makeup transfer, which aims at transplanting the makeup from the reference face to the source face while preserving the identity of the source.
The MSR-Video to Text Dataset with Clean Annotations
We cleaned the MSR-VTT annotations by removing these problems, then tested several typical video captioning models on the cleaned dataset.
TextScanner: Reading Characters in Order for Robust Scene Text Recognition
Driven by deep learning and the large volume of data, scene text recognition has evolved rapidly in recent years.
Ranked #19 on
Scene Text Recognition
on ICDAR2015
A Semantics-Assisted Video Captioning Model Trained with Scheduled Sampling
Given the features of a video, recurrent neural networks can be used to automatically generate a caption for the video.
Double-Coupling Learning for Multi-Task Data Stream Classification
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream.
Multi-View Fuzzy Clustering with The Alternative Learning between Shared Hidden Space and Partition
How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge.
Locality Preserving Projections for Grassmann manifold
Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos.
Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two datasets.
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation
The paper addresses the problem of optimizing a class of composite functions on Riemannian manifolds and a new first order optimization algorithm (FOA) with a fast convergence rate is proposed.
The Extended UCB Policies for Frequentist Multi-armed Bandit Problems
The multi-armed bandit (MAB) problem is a widely studied model in the field of operations research for sequential decision making and reinforcement learning.
