1 citations · 2 across the 12 of their papers we have counts for
14 papers
VTrajEmbed: Aligning Text with Video Object Trajectories via Spatiotemporal Embeddings
Ruizhi Chen, Zining Fan, Yudi Guo +5
Fine-grained alignment between text and video object trajectories is a core challenge in multimodal understanding. Existing embedding models excel at global retrieval, yet their si…
MeSD: Multi-Evidence Self-Distillation for VideoLLM
Weijie Zhu, Han Fang, Hanyu Fu +13
While reinforcement learning with verifiable rewards provides reliable outcome supervision for VideoLLMs, sequence-level rewards offer limited token-level guidance. On-policy self-…
TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
Peng Cai, Zhaofan Zou, Shifa Liu +7
Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-Language Models (VLMs) have significantly…
GeoAnchor: Collaborative Reasoning via Latent Decomposition for 3D Spatial Understanding
Hao Li, Han Fang, Zixin Pan +8
Although multimodal large language models (MLLMs) have achieved remarkable progress, understanding 3D spatial relationships from 2D images remains a critical challenge. Existing me…
Boosting Robust AIGI Detection with LoRA-based Pairwise Training
Ruiyang Xia, Qi Zhang, Yaowen Xu +4
The proliferation of highly realistic AI-Generated Image (AIGI) has necessitated the development of practical detection methods. While current AIGI detectors perform admirably on c…
WAT: Online Video Understanding Needs Watching Before Thinking
Zifan Han, Hongbo Sun, Jinglin Xu +6
Multimodal Large Language Models (MLLMs) have shown strong capabilities in image understanding, motivating recent efforts to extend them to video reasoning. However, existing Video…
RefBench-PRO: Perceptual and Reasoning Oriented Benchmark for Referring Expression Comprehension
Tianyi Gao, Hao Li, Han Fang +8
Referring Expression Comprehension (REC) is a vision-language task that localizes a specific image region based on a textual description. Existing REC benchmarks primarily evaluate…
Adaptive Evidential Learning for Temporal-Semantic Robustness in Moment Retrieval
Haojian Huang, Kaijing Ma, Jin Chen +8
In the domain of moment retrieval, accurately identifying temporal segments within videos based on natural language queries remains challenging. Traditional methods often employ pr…
Structure-Aware Prototype Guided Trusted Multi-View Classification
Haojian Huang, Jiahao Shi, Zhe Liu +4
Trustworthy multi-view classification (TMVC) addresses the challenge of achieving reliable decision-making in complex scenarios where multi-source information is heterogeneous, inc…
Detailed Object Description with Controllable Dimensions
Xinran Wang, Haiwen Zhang, Baoteng Li +5
Object description plays an important role for visually impaired individuals to understand and compare the differences between objects. Recent multimodal large language models(MLLM…
Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classification
Haojian Huang, Chuanyu Qin, Zhe Liu +6
Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process.…
Disentangle and denoise: Tackling context misalignment for video moment retrieval
Kaijing Ma, Han Fang, Xianghao Zang +7
Video Moment Retrieval, which aims to locate in-context video moments according to a natural language query, is an essential task for cross-modal grounding. Existing methods focus…
ProTA: Probabilistic Token Aggregation for Text-Video Retrieval
Han Fang, Xianghao Zang, Chao Ban +5
Text-video retrieval aims to find the most relevant cross-modal samples for a given query. Recent methods focus on modeling the whole spatial-temporal relations. However, since vid…
Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
Shilin Yan, Renrui Zhang, Ziyu Guo +7
Recently, video object segmentation (VOS) referred by multi-modal signals, e.g., language and audio, has evoked increasing attention in both industry and academia. It is challengin…