Search Results for author:
Found 17 papers, 15 papers with code
Learning Universal Features for Generalizable Image Forgery Localization
In recent years, advanced image editing and generation methods have rapidly evolved, making detecting and locating forged image content increasingly challenging.
Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge
Recent advances in Large Language Models (LLMs) have enabled the development of Video-LLMs, advancing multimodal learning by bridging video data with language tasks.
The Devil is in Temporal Token: High Quality Video Reasoning Segmentation
Existing methods for Video Reasoning Segmentation rely heavily on a single special token to represent the object in the keyframe or the entire video, inadequately capturing spatial complexity and inter-frame motion.
Ranked #1 on
Referring Video Object Segmentation
on ReVOS
3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene Understanding
Multi-modal Large Language Models (MLLMs) exhibit impressive capabilities in 2D tasks, yet encounter challenges in discerning the spatial positions, interrelations, and causal logic in scenes when transitioning from 2D to 3D representations.
Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
In this paper, we address the challenges in unsupervised video object segmentation (UVOS) by proposing an efficient algorithm, termed MTNet, which concurrently exploits motion and temporal cues.
AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation
To perform multi-modal fusion, we propose the Modality Aggregation Decoder, leveraging the Vision-to-Audio Fusion Block to integrate visual features into audio features across both frame and temporal levels.
Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation
In this work, we introduce Open-Vocabulary Remote Sensing Image Semantic Segmentation (OVRSISS), which aims to segment arbitrary semantic classes in remote sensing images.
Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding
Injecting semantics into 3D Gaussian Splatting (3DGS) has recently garnered significant attention.
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting
Extensive experiments demonstrate that DreamMix effectively balances identity preservation and attribute editability across various application scenarios, including object insertion, attribute editing, and small object inpainting.
LLMs Can Evolve Continually on Modality for X-Modal Reasoning
Multimodal Large Language Models (MLLMs) have gained significant attention due to their impressive capabilities in multimodal understanding.
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning
Parameter-efficient transfer learning (PETL) has emerged as a flourishing research field for adapting large pre-trained models to downstream tasks, greatly reducing trainable parameters while grappling with memory challenges during fine-tuning.
Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters
Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset.
StableIdentity: Inserting Anybody into Anywhere at First Sight
We believe that the proposed StableIdentity is an important step to unify image, video, and 3D customized generation models.
CTVIS: Consistent Training for Online Video Instance Segmentation
The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS).
Ranked #3 on
Video Instance Segmentation
on Youtube-VIS 2022 Validation
(using extra training data)
Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation
SSNet consists of a segmentation network (SN) and a saliency aggregation module (SAM).
Multi-source weak supervision for saliency detection
To this end, we propose a unified framework to train saliency detection models with diverse weak supervision sources.
Boundary-guided Feature Aggregation Network for Salient Object Detection
Fully convolutional networks (FCN) has significantly improved the performance of many pixel-labeling tasks, such as semantic segmentation and depth estimation.
