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Found 10 papers, 4 papers with code
Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond
Expanding on our findings, we introduce "Vision Description Prompting," a method that effectively improves performance in challenging vision-related tasks.
Zero-Shot Continuous Prompt Transfer: Generalizing Task Semantics Across Language Models
Prompt tuning in natural language processing (NLP) has become an increasingly popular method for adapting large language models to specific tasks.
Unsupervised Chunking with Hierarchical RNN
Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks.
A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer
Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one.
New Benchmark for Household Garbage Image Recognition
Household garbage images are usually faced with complex backgrounds, variable illuminations, diverse angles, and changeable shapes, which bring a great difficulty in garbage image classification.
Towards Multi-Domain Single Image Dehazing via Test-Time Training
Motivated by this observation, we propose a test-time training method which leverages a helper network to assist the dehazing model in better adapting to a domain of interest.
Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic
In this work, we address the explainability of NLI by weakly supervised logical reasoning, and propose an Explainable Phrasal Reasoning (EPR) approach.
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing
We also propose a novel intra-task knowledge transfer mechanism that can memorize and take advantage of synthetic domain knowledge to assist the learning process on the translated data.
An Improved Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search
We propose using more advanced methods to discriminate between "good" and "bad" sample runs, with the goal of increasing the correlation of the chosen run with the a-posteriori best one.
Stochastic Runtime Analysis of a Cross Entropy Algorithm for Traveling Salesman Problems
For simple instances that have a $\{1, n\}$-valued distance function and a unique optimal solution, we prove a stochastic runtime of $O(n^{6+\epsilon})$ with the vertex-based random solution generation, and a stochastic runtime of $O(n^{3+\epsilon}\ln n)$ with the edge-based random solution generation for an arbitrary $\epsilon\in (0, 1)$.
