360 citations · 645 across the 47 of their papers we have counts for
9 papers · 1 filter
GenView++: Unifying Adaptive Generative Augmentation and Quality-Driven Supervision for Contrastive Representation Learning
Xiaojie Li, Bei Wang, Wei Liu +4
The success of contrastive learning depends on the construction and utilization of high-quality positive pairs. However, current methods face critical limitations on two fronts: on…
FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding
Xusheng He, Wei Liu, Shanshan Ma +3
Fine-grained analysis of complex and high-speed sports like badminton presents a significant challenge for Multimodal Large Language Models (MLLMs), despite their notable advanceme…
A Survey on Video Temporal Grounding with Multimodal Large Language Model
Jianlong Wu, Wei Liu, Ye Liu +4
The recent advancement in video temporal grounding (VTG) has significantly enhanced fine-grained video understanding, primarily driven by multimodal large language models (MLLMs).…
Self-Enhanced Image Clustering with Cross-Modal Semantic Consistency
Zihan Li, Wei Sun, Jing Hu +3
While large language-image pre-trained models like CLIP offer powerful generic features for image clustering, existing methods typically freeze the encoder. This creates a fundamen…
AffordGrasp: In-Context Affordance Reasoning for Open-Vocabulary Task-Oriented Grasping in Clutter
Yingbo Tang, Shuaike Zhang, Xiaoshuai Hao +4
Inferring the affordance of an object and grasping it in a task-oriented manner is crucial for robots to successfully complete manipulation tasks. Affordance indicates where and ho…
AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language Understanding
Xiao Wang, Qingyi Si, Jianlong Wu +3
Multimodal Large Language Models (MLLMs) have revolutionized video understanding, yet are still limited by context length when processing long videos. Recent methods compress video…
MegaSR: Mining Customized Semantics and Expressive Guidance for Real-World Image Super-Resolution
Xinrui Li, Jinrong Zhang, Jianlong Wu +3
Text-to-image (T2I) models have ushered in a new era of real-world image super-resolution (Real-ISR) due to their rich internal implicit knowledge for multimodal learning. Although…
HAIC: Improving Human Action Understanding and Generation with Better Captions for Multi-modal Large Language Models
Xiao Wang, Jingyun Hua, Weihong Lin +5
Recent Multi-modal Large Language Models (MLLMs) have made great progress in video understanding. However, their performance on videos involving human actions is still limited by t…
Continuous Knowledge-Preserving Decomposition with Adaptive Layer Selection for Few-Shot Class-Incremental Learning
Xiaojie Li, Jianlong Wu, Yue Yu +2
Few-Shot Class-Incremental Learning (FSCIL) faces a critical challenge: balancing the retention of prior knowledge with the acquisition of new classes. Existing methods either free…