1 citations · 2 across the 18 of their papers we have counts for
19 papers
Acoustically Grounded Cost Learning for Open-Vocabulary Audio-Visual Semantic Segmentation
Tianrui Hui, Shaofei Huang, Qisong Han +6
Open-Vocabulary Audio-Visual Semantic Segmentation (OV-AVSS) aims to perform pixel-level segmentation of sound-emitting objects from an open set of categories. The previous method…
Parse, Search, and Confirmation: Training-Free Aerial Vision-and-Dialog Navigation with Chain-of-Thought Reasoning and Structured Spatial Memory
Yu Qi, Hongyu Li, Shaofei Huang +6
In this paper, we tackle the Aerial Vision-and-Dialog Navigation (AVDN) task in the training-free setting for resource-efficient high-altitude UAV navigation.Naively applying MLLMs…
Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets
Kaifeng Chen, Lechao Cheng, Jiyang Li +6
Dataset distillation (DD) condenses large corpora into compact, information-rich subsets for efficient training and reuse. However, under noisy supervision, DD risks condensing cor…
CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
Jinjie Shen, Yaxiong Wang, Yujiao Wu +5
The rapid rise of generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social stability. Existing detection m…
OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics
Jinjie Shen, Zheng Huang, Yuchen Zhang +7
Existing vision-language forgery detection and grounding methods operate under a closed-world paradigm, assuming verification can be completed by the model alone. However, self-con…
Towards Multi-View Sign Language Understanding: A Benchmark Dataset and Baseline
Xu Wang, Shengeng Tang, Wan Jiang +3
Most existing Sign Language Understanding (SLU) methods are developed under fixed-view settings and remain vulnerable to viewpoint changes, while the scarcity of multi-view data hi…
OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL
Jinjie Shen, Jing Wu, Yaxiong Wang +5
Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and videos prevalent in real-world misinform…
TDEdit: A Unified Diffusion Framework for Text-Drag Guided Image Manipulation
Qihang Wang, Yaxiong Wang, Lechao Cheng +1
This paper explores image editing under the joint control of text and drag interactions. While recent advances in text-driven and drag-driven editing have achieved remarkable progr…
Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations
Jinjie Shen, Yaxiong Wang, Lechao Cheng +2
The detection and grounding of manipulated content in multimodal data has emerged as a critical challenge in media forensics. While existing benchmarks demonstrate technical progre…
Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline
Haiyang Li, Yaxiong Wang, Shengeng Tang +6
Detecting deceptive multimodal content on social media has become an increasingly important problem. Two major types of deception dominate: human-crafted misinformation (e.g., rumo…
FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter
Junxi Wang, Yaxiong Wang, Lechao Cheng +1
We present FakeSV-VLM in this paper, a new VLM-based framework for detecting fake news on short video platforms. Despite significant efforts to combat this issue due to the severe…
SSAM: Self-Supervised Association Modeling for Test-Time Adaption
Yaxiong Wang, Zhenqiang Zhang, Lechao Cheng +3
Test-time adaption (TTA) has witnessed important progress in recent years, the prevailing methods typically first encode the image and the text and design strategies to model the a…
Towards Micro-Action Recognition with Limited Annotations: An Asynchronous Pseudo Labeling and Training Approach
Yan Zhang, Lechao Cheng, Yaxiong Wang +2
Micro-Action Recognition (MAR) aims to classify subtle human actions in video. However, annotating MAR datasets is particularly challenging due to the subtlety of actions. To this…
Knowledge Swapping via Learning and Unlearning
Mingyu Xing, Lechao Cheng, Shengeng Tang +3
We introduce \textbf{Knowledge Swapping}, a novel task designed to selectively regulate knowledge of a pretrained model by enabling the forgetting of user\-specified information, r…
Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning
Fangwen Wu, Lechao Cheng, Shengeng Tang +4
Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones. Balancing flexibility and stability…
ASAP: Advancing Semantic Alignment Promotes Multi-Modal Manipulation Detecting and Grounding
Zhenxing Zhang, Yaxiong Wang, Lechao Cheng +3
We present ASAP, a new framework for detecting and grounding multi-modal media manipulation (DGM4).Upon thorough examination, we observe that accurate fine-grained cross-modal sema…
Modality Alignment Meets Federated Broadcasting
Yuting Ma, Shengeng Tang, Xiaohua Xu +1
Federated learning (FL) has emerged as a powerful approach to safeguard data privacy by training models across distributed edge devices without centralizing local data. Despite adv…
EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning
Yaxiong Wang, Yujiao Wu, Lianwei Wu +3
Recent advancements in image-text matching have been notable, yet prevailing models predominantly cater to broad queries and struggle with accommodating fine-grained query intentio…
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
Yuting Ma, Lechao Cheng, Yaxiong Wang +3
Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server while keeping raw data local…