360 citations · 645 across the 47 of their papers we have counts for
50 papers
Report of the 8th LSVOS Challenge: Complex and Multimodal Video Object Segmentation
Chang Liu, Henghui Ding, Lingyi Hong +36
This report summarizes the 8th Large-scale Video Object Segmentation (LSVOS) Challenge, held in conjunction with ECCV 2026. The challenge evaluates video segmentation in three comp…
Key-Frame Reasoning with SAM3: Third Place Solution for the MeViS-Text Track of the 8th LSVOS Challenge
Ce Bian, Xusheng He, Jinrong Zhang +3
This report presents a two-stage, training-free solution for the MeViS-Text track of the 8th LSVOS Challenge. The task requires a model to localize and segment the object specified…
VOS-Agent: The 1st Place Solution for the 8th LSVOS Challenge (MOSEv2 Track)
Canyang Wu, Jinrong Zhang, Xusheng He +3
Complex video object segmentation requires robust target propagation under severe occlusion, disappearance and reappearance. Although SAM3 provides strong promptable mask propagati…
Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning
Shuaike Zhang, Shaokun Wang, Haoyu Tang +2
Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behaviors under closed-loop control. In ECL…
TimeLogic Challenge @ CVPR 2026: Strong MLLMs Meet Evidence-Seeking Agents for Temporal-Logic Video Question Answering
Zhaoyang Xu, Xusheng He, Wei Liu +2
Temporal-logic video question answering requires a model to reason about when actions occur relative to one another, such as before, after, until, since, overlap, and multi-event c…
Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding
Chang Liu, Henghui Ding, Nikhila Ravi +40
This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, whi…
The 1st Winner for 5th PVUW MeViS-Text Challenge: Strong MLLMs Meet SAM3 for Referring Video Object Segmentation
Xusheng He, Canyang Wu, Jinrong Zhang +3
This report presents our winning solution to the 5th PVUW MeViS-Text Challenge. The track studies referring video object segmentation under motion-centric language expressions, whe…
Advancing Complex Video Object Segmentation via Tracking-Enhanced Prompt: The 1st Winner for 5th PVUW MOSE Challenge
Jinrong Zhang, Canyang Wu, Xusheng He +3
In the Complex Video Object Segmentation task, researchers are required to track and segment specific targets within cluttered environments, which rigorously tests a method's capab…
StructAlign: Structured Cross-Modal Alignment for Continual Text-to-Video Retrieval
Shaokun Wang, Weili Guan, Jizhou Han +3
Continual Text-to-Video Retrieval (CTVR) is a challenging multimodal continual learning setting, where models must incrementally learn new semantic categories while maintaining acc…
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…
ReTaKe: Reducing Temporal and Knowledge Redundancy for Long Video Understanding
Xiao Wang, Qingyi Si, Jianlong Wu +3
Video Large Language Models (VideoLLMs) have made significant strides in video understanding but struggle with long videos due to the limitations of their backbone LLMs. Existing s…
RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training
Muhe Ding, Yang Ma, Pengda Qin +3
Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tas…
Preview-based Category Contrastive Learning for Knowledge Distillation
Muhe Ding, Jianlong Wu, Xue Dong +4
Knowledge distillation is a mainstream algorithm in model compression by transferring knowledge from the larger model (teacher) to the smaller model (student) to improve the perfor…
Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding
Xiao Wang, Jianlong Wu, Zijia Lin +3
Recently, video-language understanding has achieved great success through large-scale pre-training. However, data scarcity remains a prevailing challenge. This study quantitatively…
DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture
Qianlong Xiang, Miao Zhang, Yuzhang Shang +3
Diffusion models (DMs) have demonstrated exceptional generative capabilities across various domains, including image, video, and so on. A key factor contributing to their effective…
Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning
Xiaojie Li, Yibo Yang, Jianlong Wu +4
Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples while preserving knowledge of previously learned classes. Existing metho…