18 accepted papers
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 methods rely heavily on manipulation-specific models and large-scale labeled data, resulting in poor generalization to em…
Vision Foundation Models (VFMs) provide rich and transferable representations through large-scale pretraining, yet their high-capacity representations remain underutilized when adapted to downstream tasks. In Domain Generalization Semantic Segmentation (DGSS), parameter-efficient fine-tuning (PEFT)
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 misinformation. To bridge this gap, we propose **OmniVL-Guard**, a unified framework for omni vision-language forgery detection a…
Domain Generalization in Semantic Segmentation (DG-SS) aims to enable segmentation models to perform robustly in unseen environments. However, conventional DG-SS methods are restricted to a fixed set of known categories, limiting their applicability in open-world scenarios. Recent progress in Vision
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 using labeled data for known types and unlabeled data containing a mixture of known and novel types. However, existing
Person De-Reidentification (De-ReID) is an emerging and safety-critical task that aims to selectively forget specific individuals in surveillance systems while preserving the recognition capability for others. Existing methods typically learn both forgetting and retaining objectives within a unified
Generalized Category Discovery (GCD) leverages labeled data to categorize unlabeled samples from known or unknown classes. Most previous methods jointly optimize supervised and unsupervised objectives and achieve promising results. However, inherent optimization interference still limits their abili
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 that may belong to both known and unknown categories, utilizing the category knowledge from only labeled data. Existing OC…
As a leading unsupervised classification algorithm in artificial intelligence, multi-view subspace clustering segments unlabeled data from different subspaces. Recent works based on the anchor have been proposed to decrease the computation complexity for the datasets with large scales in multi-view…
Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes. To meet the recent decentralization trend in the community we introduce a practical yet challenging task Federated GCD (Fed-GCD) where the training data are dis…
"Generalized Category Discovery (GCD) utilizes labelled data from seen categories to cluster unlabelled samples from both seen and unseen categories. Previous methods have demonstrated that assigning pseudo-labels for representation learning is effective. However, these methods commonly predict pseu…
Ultra-fine-grained visual categorization (Ultra-FGVC) aims at distinguishing highly similar sub-categories within fine-grained objects such as different soybean cultivars. Compared to traditional fine-grained visual categorization Ultra-FGVC encounters more hurdles due to the small inter-class and l…
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 unknown classes, by leveraging only known category knowledge contained in labeled data. Previous OCD methods employ the…
"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 labeled data from known categories. Current GCD methods rely on only visual cues, which however neglect the multi-modality…
Audio-Visual Question Answering (AVQA) is a sophisticated QA task, which aims at answering textual questions over given video-audio pairs with comprehensive multimodal reasoning. Through detailed causal-graph analyses and careful inspections of their learning processes, we reveal that AVQA models ar…
Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This…
Current VQA models are suffering from the problem of overdependence on language bias, which severely reduces their robustness in real-world scenarios. In this paper, we analyze VQA models from the view of forward/backward chaining in the inference engine, and propose to enhance their robustness via…
Person ReID methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are ineffective because the domain is continually changing in which case incremental learning over multiple domains is required poten…