Our Publications

ConViS-Bench: Estimating Video Similarity Through Semantic Concepts

ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression

Increasing the Utility of Synthetic Images through Chamfer Guidance

SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting

Towards a General Attention Framework on Gyrovector Spaces for Matrix Manifolds

When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding

AlignCAT: Visual-Linguistic Alignment of Category and Attributefor Weakly Supervised Visual Grounding

Dynamic Scoring with Enhanced Semantics for Training-Free Human-Object Interaction Detection

FreeInsert: Disentangled Text-Guided Object Insertion in 3D Gaussian Scene without Spatial Priors

Unveiling Open-set Noise: Theoretical Insights into Label Noise

FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery

Hierarchical Visual Prompt Learning for Continual Video Instance Segmentation

On Large Multimodal Models as Open-World Image Classifiers

Pseudo-SD: Pseudo Controlled Stable Diffusion for Semi-Supervised and Cross-Domain Semantic Segmentation

SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining

Superpowering Open-Vocabulary Object Detectors for X-ray Vision

What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models

Automatic benchmarking of large multimodal models via iterative experiment programming

Diversified in-domain synthesis with efficient fine-tuning for few-shot classification

Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers

Compositional Caching for Training-free Open-vocabulary Attribute Detection

Multi-focal Conditioned Latent Diffusion for Person Image Synthesis

Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models