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Found 21 papers, 14 papers with code
UnfoldIR: Rethinking Deep Unfolding Network in Illumination Degradation Image Restoration
Deep unfolding networks (DUNs) are widely employed in illumination degradation image restoration (IDIR) to merge the interpretability of model-based approaches with the generalization of learning-based methods.
UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
Furthermore, we adopt a simple concatenation operation to integrate the reference appearance into the model and incorporate the pose information of the reference image for enhanced pose alignment.
Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models
Specifically, the instruction condition is injected into the grouped visual tokens at the local level and the learnable tokens at the global level, and we conduct the attention mechanism to complete the conditional compression.
Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?
Together, our LSDBench and RHS framework address the unique challenges of high-NSD long-video tasks, setting a new standard for evaluating and improving LVLMs in this domain.
DreamRelation: Relation-Centric Video Customization
First, in Relational Decoupling Learning, we disentangle relations from subject appearances using relation LoRA triplet and hybrid mask training strategy, ensuring better generalization across diverse relationships.
Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts
Image assessment aims to evaluate the quality and aesthetics of images and has been applied across various scenarios, such as natural and AIGC scenes.
Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
UniLearner exploits multimodal data unrelated to the COS task to improve the segmentation ability of the COS models by generating pseudo-modal content and cross-modal semantic associations.
RUN: Reversible Unfolding Network for Concealed Object Segmentation
To address this, we propose the Reversible Unfolding Network (RUN), which applies reversible strategies across both mask and RGB domains through a theoretically grounded framework, enabling accurate segmentation.
Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition
As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI.
Ranked #1 on
Visual Question Answering (VQA)
on EgoSchema
InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences
They struggle to maintain consistency in both the foreground and background during concept swapping, especially when the shape difference is large between objects.
A Survey of Camouflaged Object Detection and Beyond
Camouflaged Object Detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for computer vision systems.
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models
To address this problem efficiently, we propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of VLMs from a perspective of avoiding information interference.
Diffusion Models in Low-Level Vision: A Survey
Deep generative models have garnered significant attention in low-level vision tasks due to their generative capabilities.
Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network
Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings.
UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment
Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) aim to simulate human subjective perception of image visual quality and aesthetic appeal.
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components.
Consistency Regularization for Generalizable Source-free Domain Adaptation
In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets.
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module.
Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher
This paper aims to address these two issues by proposing the Class-Balanced Mean Teacher (CBMT) model.
Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning.
Camouflaged Object Detection With Feature Decomposition and Edge Reconstruction
COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries.
