12 papers
CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery
Yuanpei Liu, Zhenqi He, Jialu Tang +1
Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially labelled data, the goal is to correctly recognize k…
Multimodal Latent Reasoning via Hierarchical Visual Cues Injection
Yiming Zhang, Qiangyu Yan, Borui Jiang +1
The advancement of multimodal large language models (MLLMs) has enabled impressive perception capabilities. However, their reasoning process often remains a "fast thinking" paradig…
HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices
HyperAI Team, Yuchen Liu, Kaiyang Han +26
Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy direc…
Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
Yifan Song, Fenglin Yu, Yihong Luo +4
Incomplete node features are ubiquitous in real-world scenarios such as user profiling and cold-start recommendation, which severely hinders the practical deployment of graph learn…
VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models
Silin Cheng, Kai Han
Vision-language models (VLMs), such as CLIP, have shown strong generalization under zero-shot settings, yet adapting them to downstream tasks with limited supervision remains a sig…
MOGRAS: Human Motion with Grasping in 3D Scenes
Kunal Bhosikar, Siddharth Katageri, Vivek Madhavaram +2
Generating realistic full-body motion interacting with objects is critical for applications in robotics, virtual reality, and human-computer interaction. While existing methods can…
SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery
Zhenqi He, Yuanpei Liu, Kai Han
This paper investigates the problem of Generalized Category Discovery (GCD). Given a partially labelled dataset, GCD aims to categorize all unlabelled images, regardless of whether…
Category Discovery: An Open-World Perspective
Zhenqi He, Yuanpei Liu, Kai Han
Category discovery (CD) is an emerging open-world learning task, which aims at automatically categorizing unlabelled data containing instances from unseen classes, given some label…
PartCo: Part-Level Correspondence Priors Enhance Category Discovery
Fernando Julio Cendra, Kai Han
Generalized Category Discovery (GCD) aims to identify both known and novel categories within unlabeled data by leveraging a set of labeled examples from known categories. Existing…
When Deepfake Detection Meets Graph Neural Network:a Unified and Lightweight Learning Framework
Haoyu Liu, Chaoyu Gong, Mengke He +3
The proliferation of generative video models has made detecting AI-generated and manipulated videos an urgent challenge. Existing detection approaches often fail to generalize acro…
Ascending the Infinite Ladder: Benchmarking Spatial Deformation Reasoning in Vision-Language Models
Jiahuan Zhang, Shunwen Bai, Tianheng Wang +4
Humans naturally possess the spatial reasoning ability to form and manipulate images and structures of objects in space. There is an increasing effort to endow Vision-Language Mode…
Generalized Category Discovery under the Long-Tailed Distribution
Bingchen Zhao, Kai Han
This paper addresses the problem of Generalized Category Discovery (GCD) under a long-tailed distribution, which involves discovering novel categories in an unlabelled dataset usin…