19 citations · 40 across the 10 of their papers we have counts for
12 papers
MicroSDF: Microfacet-Driven Hybrid Neural SDFs for Mixed-Reflectance Surface Reconstruction
Lejia Ye, Yuhua Xu, Yulan Guo +2
Accurate 3D reconstruction in real-world environments remains a significant challenge due to the coexistence of reflective and non-reflective surfaces, which pose distinct modeling…
Warp-free Cross-view Geo-localization via Feature-space Consensus Mining
Zhuo Song, Lian Xu, Runqing Jiang +4
Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imagery. Although existing methods…
Learning Structured Visual Compositional Representations for Weakly Supervised Referring Expression Comprehension
Lian Xu, Mohammed Bennamoun, Farid Boussaid +3
Referring expression comprehension (REC) aims to localize the object in an image described by natural language. In Weakly supervised REC (WREC), existing approaches primarily opera…
ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers
Changjun Li, Runqing Jiang, Lian Xu +3
Vision Transformers have achieved remarkable success in many fields, yet their deployment on edge devices remains challenging due to their substantial computational demands. Post-T…
Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs
Wenjie Zhu, Yabin Zhang, Liang Xu +3
While test-time adaptation (TTA) empowers vision-language models to adapt without costly retraining, it remains highly vulnerable to out-of-distribution (OOD) outliers prevalent in…
Fact or Fake? Assessing the Role of Deepfake Detectors in Multimodal Misinformation Detection
A S M Sharifuzzaman Sagar, Mohammed Bennamoun, Farid Boussaid +4
In multimodal misinformation, deception usually arises not just from pixel-level manipulations in an image, but from the semantic and contextual claim jointly expressed by the imag…
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation
Lian Xu, Mohammed Bennamoun, Farid Boussaid +3
Most existing weakly supervised semantic segmentation (WSSS) methods rely on Class Activation Mapping (CAM) to extract coarse class-specific localization maps using image-level lab…
MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation
Lian Xu, Mohammed Bennamoun, Farid Boussaid +3
This paper proposes a novel transformer-based framework that aims to enhance weakly supervised semantic segmentation (WSSS) by generating accurate class-specific object localizatio…
Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods
Laurent Jospin, Allen Antony, Lian Xu +3
In stereo vision, self-similar or bland regions can make it difficult to match patches between two images. Active stereo-based methods mitigate this problem by projecting a pseudo-…
A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance
Aref Miri Rekavandi, Lian Xu, Farid Boussaid +3
Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and ident…
Multi-class Token Transformer for Weakly Supervised Semantic Segmentation
Lian Xu, Wanli Ouyang, Mohammed Bennamoun +2
This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired…
Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation
Lian Xu, Wanli Ouyang, Mohammed Bennamoun +3
Semantic segmentation is a challenging task in the absence of densely labelled data. Only relying on class activation maps (CAM) with image-level labels provides deficient segmenta…