3 citations · 6 across the 13 of their papers we have counts for
12 papers · 1 filter
When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization
Tianqi Li, Wenyu Fang, Xin He +3
Camouflaged object detection (COD) segments objects that intentionally blend with the background, so predictions depend on subtle texture and boundary cues. COD is often needed und…
Learning More by Seeing Less: Structure First Learning for Efficient, Transferable, and Human-Aligned Vision
Tianqin Li, George Liu, Tai Sing Lee
Despite remarkable progress in computer vision, modern recognition systems remain fundamentally limited by their dependence on rich, redundant visual inputs. In contrast, humans ca…
ViT-Split: Unleashing the Power of Vision Foundation Models via Efficient Splitting Heads
Yifan Li, Xin Li, Tianqin Li +3
Vision foundation models (VFMs) have demonstrated remarkable performance across a wide range of downstream tasks. While several VFM adapters have shown promising results by leverag…
Perceptual Inductive Bias Is What You Need Before Contrastive Learning
Tianqin Li, Junru Zhao, Dunhan Jiang +3
David Marr's seminal theory of human perception stipulates that visual processing is a multi-stage process, prioritizing the derivation of boundary and surface properties before fo…
From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models
Tianqin Li, Ziqi Wen, Leiran Song +3
Human vision organizes local cues into coherent global forms using Gestalt principles like closure, proximity, and figure-ground assignment -- functions reliant on global spatial s…
Learning Transferable Negative Prompts for Out-of-Distribution Detection
Tianqi Li, Guansong Pang, Xiao Bai +2
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead…
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Zicheng Zhang, Ruobing Zheng, Ziwen Liu +7
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences. These i…
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning
Wenjun Miao, Guansong Pang, Tianqi Li +2
Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are…
Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
Tianqin Li, Ziqi Wen, Yangfan Li +1
Current deep-learning models for object recognition are known to be heavily biased toward texture. In contrast, human visual systems are known to be biased toward shape and structu…
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configuration
Ziqi Wen, Tianqin Li, Zhi Jing +1
Deep learning models are known to exhibit a strong texture bias, while human tends to rely heavily on global shape structure for object recognition. The current benchmark for evalu…
Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds
Tianqi Li, Guansong Pang, Xiao Bai +3
Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-worl…
SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators
Andrew Luo, Tianqin Li, Wen-Hao Zhang +1
Recent advances in deep generative models have led to immense progress in 3D shape synthesis. While existing models are able to synthesize shapes represented as voxels, point-cloud…