38 citations · 92 across the 25 of their papers we have counts for
7 papers · 1 filter
Preserving Cross-Modal Consistency for CLIP-based Class-Incremental Learning
Haoran Chen, Houze Xu, Micah Goldblum +2
Class-incremental learning (CIL) enables models to continuously learn new categories from sequential tasks without forgetting previously acquired knowledge. While recent advances i…
Multi-Prompt Progressive Alignment for Multi-Source Unsupervised Domain Adaptation
Haoran Chen, Zexiao Wang, Haidong Cao +2
Large Vision-Language Models like CLIP have become a powerful foundation for Unsupervised Domain Adaptation due to their strong zero-shot generalization. State-of-the-art methods t…
Multimodal Language Models See Better When They Look Shallower
Haoran Chen, Junyan Lin, Xinghao Chen +6
Multimodal large language models (MLLMs) typically extract visual features from the final layers of a pretrained Vision Transformer (ViT). This widespread deep-layer bias, however,…
Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices
Junyan Lin, Haoran Chen, Yue Fan +5
Multimodal Large Language Models (MLLMs) have made significant advancements in recent years, with visual features playing an increasingly critical role in enhancing model performan…
EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation
Zihao Zhang, Haoran Chen, Haoyu Zhao +4
Handling complex or nonlinear motion patterns has long posed challenges for video frame interpolation. Although recent advances in diffusion-based methods offer improvements over t…
Achieving More with Less: Additive Prompt Tuning for Rehearsal-Free Class-Incremental Learning
Haoran Chen, Ping Wang, Zihan Zhou +3
Class-incremental learning (CIL) enables models to learn new classes progressively while preserving knowledge of previously learned ones. Recent advances in this field have shifted…
A Benchmark for Crime Surveillance Video Analysis with Large Models
Haoran Chen, Dong Yi, Moyan Cao +3
Anomaly analysis in surveillance videos is a crucial topic in computer vision. In recent years, multimodal large language models (MLLMs) have outperformed task-specific models in v…