19 papers
Localize Any Object in X-Ray Security Scans without Human Annotation
Yaqi Cai, Mingxuan Liu, Lorenzo Vaquero +5
Universal object localization in X-ray security inspection is critical for automated threat detection in safety-critical venues. However, unlike everyday RGB images that dominate w…
Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation
Lingrui Li, Nan Pu, Dong Zhao +4
Test-time adaptation (TTA) aims to mitigate distribution shifts by adapting models with unlabeled target data at inference time. While TTA with vision-language models (VLMs) has sh…
Technical Report for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Pretraining-Diverse Ensemble of Foundation Vision Encoders for Robust Outdoor Scene Understanding
Boyan Wang, Yongxi Huang, Wenjing Li +4
This report presents our solution for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, which requires parsing unstructured outdoor scenes from four camera platf…
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…
The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery
Haiyang Zheng, Nan Pu, Yaqi Cai +4
Generalized Category Discovery (GCD) leverages labeled data to categorize unlabeled samples from known or unknown classes. Most previous methods jointly optimize supervised and uns…
Open-Vocabulary Domain Generalization in Urban-Scene Segmentation
Dong Zhao, Qi Zang, Nan Pu +3
Domain Generalization in Semantic Segmentation (DG-SS) aims to enable segmentation models to perform robustly in unseen environments. However, conventional DG-SS methods are restri…
Multi-Scale Global-Instance Prompt Tuning for Continual Test-time Adaptation in Medical Image Segmentation
Lingrui Li, Yanfeng Zhou, Nan Pu +2
Distribution shift is a common challenge in medical images obtained from different clinical centers, significantly hindering the deployment of pre-trained semantic segmentation mod…
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…
Open-World Deepfake Attribution via Confidence-Aware Asymmetric Learning
Haiyang Zheng, Nan Pu, Wenjing Li +3
The proliferation of synthetic facial imagery has intensified the need for robust Open-World DeepFake Attribution (OW-DFA), which aims to attribute both known and unknown forgeries…
Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Experts
Haiyang Zheng, Nan Pu, Wenjing Li +2
Generalized Category Discovery (GCD) is an open-world problem that clusters unlabeled data by leveraging knowledge from partially labeled categories. A key challenge is that unlabe…
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…
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery
Xiao Liu, Nan Pu, Haiyang Zheng +3
In this paper, we investigate a practical yet challenging task: On-the-fly Category Discovery (OCD). This task focuses on the online identification of newly arriving stream data th…
Fire and Smoke Detection with Burning Intensity Representation
Xiaoyi Han, Yanfei Wu, Nan Pu +4
An effective Fire and Smoke Detection (FSD) and analysis system is of paramount importance due to the destructive potential of fire disasters. However, many existing FSD methods di…
Benchmarking Multi-Scene Fire and Smoke Detection
Xiaoyi Han, Nan Pu, Zunlei Feng +4
The current irregularities in existing public Fire and Smoke Detection (FSD) datasets have become a bottleneck in the advancement of FSD technology. Upon in-depth analysis, we iden…
Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery
Haiyang Zheng, Nan Pu, Wenjing Li +2
In this paper, we study a practical yet challenging task, On-the-fly Category Discovery (OCD), aiming to online discover the newly-coming stream data that belong to both known and…
Discriminative Anchor Learning for Efficient Multi-view Clustering
Yalan Qin, Nan Pu, Hanzhou Wu +1
Multi-view clustering aims to study the complementary information across views and discover the underlying structure. For solving the relatively high computational cost for the exi…
Novel Class Discovery for Ultra-Fine-Grained Visual Categorization
Yu Liu, Yaqi Cai, Qi Jia +3
Ultra-fine-grained visual categorization (Ultra-FGVC) aims at distinguishing highly similar sub-categories within fine-grained objects, such as different soybean cultivars. Compare…
Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery
Haiyang Zheng, Nan Pu, Wenjing Li +2
In this paper, we study the problem of Generalized Category Discovery (GCD), which aims to cluster unlabeled data from both known and unknown categories using the knowledge of labe…