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Found 16 papers, 6 papers with code
FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction
To address this, we propose FineMatch, a new aspect-based fine-grained text and image matching benchmark, focusing on text and image mismatch detection and correction.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Emo-Avatar: Efficient Monocular Video Style Avatar through Texture Rendering
To address the above problems, we propose the Efficient Monotonic Video Style Avatar (Emo-Avatar) through deferred neural rendering that enhances StyleGAN's capacity for producing dynamic, drivable portrait videos.
CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs
When exploring the development of Artificial General Intelligence (AGI), a critical task for these models involves interpreting and processing information from multiple image inputs.
Ranked #3 on
Visual Reasoning
on Winoground
Video Understanding with Large Language Models: A Survey
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly.
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data
We first figure out that the distribution gap between labeled and unlabeled datasets cannot be ignored, even though the two datasets are sampled from the same distribution.
GPT-4V(ision) as A Social Media Analysis Engine
Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content.
Cross Contrasting Feature Perturbation for Domain Generalization
Domain generalization (DG) aims to learn a robust model from source domains that generalize well on unseen target domains.
DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks
Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge.
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation
In Stage \uppercase\expandafter{\romannumeral1}, we train the target model from scratch with soft pseudo-labels generated by the source model in a knowledge distillation manner.
Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis.
Feature Alignment and Uniformity for Test Time Adaptation
Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples.
Prototype Knowledge Distillation for Medical Segmentation with Missing Modality
Specifically, our ProtoKD can not only distillate the pixel-wise knowledge of multi-modality data to single-modality data but also transfer intra-class and inter-class feature variations, such that the student model could learn more robust feature representation from the teacher model and inference with only one single modality data.
Bootstrap The Original Latent: Learning a Private Model from a Black-box Model
In this paper, considering the balance of data/model privacy of model owners and user needs, we propose a new setting called Back-Propagated Black-Box Adaptation (BPBA) for users to better train their private models via the guidance of the back-propagated results of a Black-box foundation/source model.
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization
Different from learning domain invariant features from source domains, we decouple the input images into Domain Expert Features and noise.
Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation
Experimental results on multiple semantic segmentation benchmarks show that our unsupervised segmentation framework specializes in catching semantic representations, which outperforms all the unpretrained and even several pretrained methods.
Ranked #1 on
Unsupervised Semantic Segmentation
on COCO-Stuff-3
