74 citations · 315 across the 165 of their papers we have counts for
25 papers · 2 filters
Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation
Dong Zhao, Ruizhi Yang, Shuang Wang +5
Presently, self-training stands as a prevailing approach in cross-domain semantic segmentation, enhancing model efficacy by training with pixels assigned with reliable pseudo-label…
Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
Victor G. Turrisi da Costa, Nicola Dall'Asen, Yiming Wang +2
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers…
ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models
Weijie Wang, Wenqi Ren, Guofeng Mei +5
State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training, which limits their practical applications in real-world scenarios and often hinders g…
Improving Fairness using Vision-Language Driven Image Augmentation
Moreno D'Incà, Christos Tzelepis, Ioannis Patras +1
Fairness is crucial when training a deep-learning discriminative model, especially in the facial domain. Models tend to correlate specific characteristics (such as age and skin col…
Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation
Wenhao Li, Mengyuan Liu, Hong Liu +3
Transformers have been successfully applied in the field of video-based 3D human pose estimation. However, the high computational costs of these video pose transformers (VPTs) make…
CNNs for JPEGs: A Study in Computational Cost
Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida
Convolutional neural networks (CNNs) have achieved astonishing advances over the past decade, defining state-of-the-art in several computer vision tasks. CNNs are capable of learni…
Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation
Lucas Fernando Alvarenga e Silva, Nicu Sebe, Jurandy Almeida
Convolutional Neural Networks (CNNs) have brought revolutionary advances to many research areas due to their capacity of learning from raw data. However, when those methods are app…
Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake Detection
Weijie Wang, Zhengyu Zhao, Nicu Sebe +1
Malicious use of deepfakes leads to serious public concerns and reduces people's trust in digital media. Although effective deepfake detectors have been proposed, they are substant…
Budget-Aware Pruning: Handling Multiple Domains with Less Parameters
Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos +2
Deep learning has achieved state-of-the-art performance on several computer vision tasks and domains. Nevertheless, it still has a high computational cost and demands a significant…
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni +3
Deep-learning models for 3D point cloud semantic segmentation exhibit limited generalization capabilities when trained and tested on data captured with different sensors or in vary…
Interactive Neural Painting
Elia Peruzzo, Willi Menapace, Vidit Goel +9
In the last few years, Neural Painting (NP) techniques became capable of producing extremely realistic artworks. This paper advances the state of the art in this emerging research…
Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis
Hao Tang, Guolei Sun, Nicu Sebe +1
We propose a novel ECGAN for the challenging semantic image synthesis task. Although considerable improvements have been achieved by the community in the recent period, the quality…
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation
Federico Betti, Jacopo Staiano, Lorenzo Baraldi +2
Research in Image Generation has recently made significant progress, particularly boosted by the introduction of Vision-Language models which are able to produce high-quality visua…
Householder Projector for Unsupervised Latent Semantics Discovery
Yue Song, Jichao Zhang, Nicu Sebe +1
Generative Adversarial Networks (GANs), especially the recent style-based generators (StyleGANs), have versatile semantics in the structured latent space. Latent semantics discover…
T2TD: Text-3D Generation Model based on Prior Knowledge Guidance
Weizhi Nie, Ruidong Chen, Weijie Wang +2
In recent years, 3D models have been utilized in many applications, such as auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its pra…
Federated Generalized Category Discovery
Nan Pu, Zhun Zhong, Xinyuan Ji +1
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 tre…
Dynamic Conceptional Contrastive Learning for Generalized Category Discovery
Nan Pu, Zhun Zhong, Nicu Sebe
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…
Attribute-preserving Face Dataset Anonymization via Latent Code Optimization
Simone Barattin, Christos Tzelepis, Ioannis Patras +1
This work addresses the problem of anonymizing the identity of faces in a dataset of images, such that the privacy of those depicted is not violated, while at the same time the dat…
Graph Transformer GANs for Graph-Constrained House Generation
Hao Tang, Zhenyu Zhang, Humphrey Shi +5
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained h…
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration
Juanjuan Weng, Zhiming Luo, Zhun Zhong +2
Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft targeted advers…
PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
Vidit Goel, Elia Peruzzo, Yifan Jiang +6
Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text, while others use low-level conditioning. Neverthel…
Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery
Mingxuan Liu, Subhankar Roy, Zhun Zhong +2
Discovering novel concepts in unlabelled datasets and in a continuous manner is an important desideratum of lifelong learners. In the literature such problems have been partially a…
SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective
Zipeng Xu, Songlong Xing, Enver Sangineto +1
Owing to the power of vision-language foundation models, e.g., CLIP, the area of image synthesis has seen recent important advances. Particularly, for style transfer, CLIP enables…
StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model
Zipeng Xu, Enver Sangineto, Nicu Sebe
Despite the progress made in the style transfer task, most previous work focus on transferring only relatively simple features like color or texture, while missing more abstract co…