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Found 13 papers, 7 papers with code
Learning Optimal Prompt Ensemble for Multi-source Visual Prompt Transfer
Prompt tuning has emerged as a lightweight adaptation strategy for adapting foundation models to downstream tasks, particularly in resource-constrained systems.
Probabilistic QoS Metric Forecasting in Delay-Tolerant Networks Using Conditional Diffusion Models on Latent Dynamics
Active QoS metric prediction, commonly employed in the maintenance and operation of DTN, could enhance network performance regarding latency, throughput, energy consumption, and dependability.
Multivariate Time Series Forecasting
Probabilistic Time Series Forecasting
+1
Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving
However, since lightweight models are crucial for autonomous driving systems, this presents a significant challenge for integrating VLMs into the field.
Exploiting Task Relationships for Continual Learning Using Transferability-Aware Task Embeddings
Continual learning (CL) has been an essential topic in the contemporary application of deep neural networks, where catastrophic forgetting (CF) can impede a model's ability to acquire knowledge progressively.
MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous Driving
Meanwhile, most existing VLMs lack the ability to process multiple images, making it difficult to adapt to multi-camera perception in autonomous driving.
First Multi-Dimensional Evaluation of Flowchart Comprehension for Multimodal Large Language Models
We propose the first comprehensive method, FlowCE, to assess MLLMs across various dimensions for tasks related to flowcharts.
Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language Models
This limitation restricts the capabilities of pretrained VLMs and can result in incorrect predictions in downstream tasks.
PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model
PSALM is a powerful extension of the Large Multi-modal Model (LMM) to address the segmentation task challenges.
Ranked #2 on
Referring Expression Segmentation
on RefCoCo val
(using extra training data)
SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector
Ablation experiments demonstrate that both of them are effective in mitigating the impact of open-world knowledge distillation on the learning of known objects.
Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training
While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts.
Looking and Listening: Audio Guided Text Recognition
Text recognition in the wild is a long-standing problem in computer vision.
Detecting the open-world objects with the help of the Brain
We propose leveraging the VL as the ``Brain'' of the open-world detector by simply generating unknown labels.
Transferability-Guided Cross-Domain Cross-Task Transfer Learning
We propose two novel transferability metrics F-OTCE (Fast Optimal Transport based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more transferable representations for cross-domain cross-task transfer learning.
