13 citations · 42 across the 49 of their papers we have counts for
19 papers · 1 filter
Memory Efficient Matting with Adaptive Token Routing
Yiheng Lin, Yihan Hu, Chenyi Zhang +5
Transformer-based models have recently achieved outstanding performance in image matting. However, their application to high-resolution images remains challenging due to the quadra…
Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection
Xinhao Zhong, Siyu Jiao, Yao Zhao +1
Current Semi-Supervised Object Detection (SSOD) methods enhance detector performance by leveraging large amounts of unlabeled data, assuming that both labeled and unlabeled data sh…
Behavior Backdoor for Deep Learning Models
Jiakai Wang, Pengfei Zhang, Renshuai Tao +5
The various post-processing methods for deep-learning-based models, such as quantification, pruning, and fine-tuning, play an increasingly important role in artificial intelligence…
BGM: Background Mixup for X-ray Prohibited Items Detection
Weizhe Liu, Renshuai Tao, Hongguang Zhu +3
Current data-driven approaches for X-ray prohibited items detection remain under-explored, particularly in the design of effective data augmentations. Existing natural image augmen…
ROSE: Revolutionizing Open-Set Dense Segmentation with Patch-Wise Perceptual Large Multimodal Model
Kunyang Han, Yibo Hu, Mengxue Qu +3
Advances in CLIP and large multimodal models (LMMs) have enabled open-vocabulary and free-text segmentation, yet existing models still require predefined category prompts, limiting…
Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?
Renshuai Tao, Haoyu Wang, Yuzhe Guo +5
To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dua…
PAD-F: Prior-Aware Debiasing Framework for Long-Tailed X-ray Prohibited Item Detection
Haoyu Wang, Renshuai Tao, Wei Wang +1
Detecting prohibited items in X-ray security imagery is a challenging yet crucial task. With the rapid advancement of deep learning, object detection algorithms have been widely ap…
Collapsed Language Models Promote Fairness
Jingxuan Xu, Wuyang Chen, Linyi Li +2
To mitigate societal biases implicitly encoded in recent successful pretrained language models, a diverse array of approaches have been proposed to encourage model fairness, focusi…
C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection
Chuangchuang Tan, Renshuai Tao, Huan Liu +4
This work focuses on AIGC detection to develop universal detectors capable of identifying various types of forgery images. Recent studies have found large pre-trained models, such…
DreamLCM: Towards High-Quality Text-to-3D Generation via Latent Consistency Model
Yiming Zhong, Xiaolin Zhang, Yao Zhao +1
Recently, the text-to-3D task has developed rapidly due to the appearance of the SDS method. However, the SDS method always generates 3D objects with poor quality due to the over-s…
Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation
Siyu Jiao, Hongguang Zhu, Jiannan Huang +3
Pre-trained vision-language models, e.g. CLIP, have been increasingly used to address the challenging Open-Vocabulary Segmentation (OVS) task, benefiting from their well-aligned vi…
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning
Man Liu, Huihui Bai, Feng Li +4
Zero-shot learning (ZSL) endeavors to transfer knowledge from seen categories to recognize unseen categories, which mostly relies on the semantic-visual interactions between image…
Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
Hanwen Liang, Yuyang Yin, Dejia Xu +5
The availability of large-scale multimodal datasets and advancements in diffusion models have significantly accelerated progress in 4D content generation. Most prior approaches rel…
ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance
Jiannan Huang, Jun Hao Liew, Hanshu Yan +4
Recent text-to-image customization works have proven successful in generating images of given concepts by fine-tuning diffusion models on a few examples. However, tuning-based meth…
Transferable and Principled Efficiency for Open-Vocabulary Segmentation
Jingxuan Xu, Wuyang Chen, Yao Zhao +1
Recent success of pre-trained foundation vision-language models makes Open-Vocabulary Segmentation (OVS) possible. Despite the promising performance, this approach introduces heavy…
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
Chuangchuang Tan, Yao Zhao, Shikui Wei +3
This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequ…
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection
Chuangchuang Tan, Ping Liu, RenShuai Tao +4
Recently, the proliferation of increasingly realistic synthetic images generated by various generative adversarial networks has increased the risk of misuse. Consequently, there is…
Learning Hierarchical Color Guidance for Depth Map Super-Resolution
Runmin Cong, Ronghui Sheng, Hao Wu +5
Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However…
Region-Adaptive Transform with Segmentation Prior for Image Compression
Yuxi Liu, Wenhan Yang, Huihui Bai +2
Learned Image Compression (LIC) has shown remarkable progress in recent years. Existing works commonly employ CNN-based or self-attention-based modules as transform methods for com…