31 citations · 78 across the 13 of their papers we have counts for
14 papers
DisCo: Disentangled Control for Realistic Human Dance Generation
Tan Wang, Linjie Li, Kevin Lin +6
Generative AI has made significant strides in computer vision, particularly in text-driven image/video synthesis (T2I/T2V). Despite the notable advancements, it remains challenging…
PaintSeg: Training-free Segmentation via Painting
Xiang Li, Chung-Ching Lin, Yinpeng Chen +3
The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training. We propose an adversarial masked contrastive painting (AMCP) process, which cr…
Neural Voting Field for Camera-Space 3D Hand Pose Estimation
Lin Huang, Chung-Ching Lin, Kevin Lin +4
We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation. As opposed to recent works, most of which first…
Adaptive Human Matting for Dynamic Videos
Chung-Ching Lin, Jiang Wang, Kun Luo +4
The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-t…
Equivariant Similarity for Vision-Language Foundation Models
Tan Wang, Kevin Lin, Linjie Li +5
This study explores the concept of equivariance in vision-language foundation models (VLMs), focusing specifically on the multimodal similarity function that is not only the major…
MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction
Kevin Lin, Chung-Ching Lin, Lin Liang +2
Traditional methods of reconstructing 3D human pose and mesh from single images rely on paired image-mesh datasets, which can be difficult and expensive to obtain. Due to this limi…
LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling
Linjie Li, Zhe Gan, Kevin Lin +4
Unified vision-language frameworks have greatly advanced in recent years, most of which adopt an encoder-decoder architecture to unify image-text tasks as sequence-to-sequence gene…
Cross-modal Representation Learning for Zero-shot Action Recognition
Chung-Ching Lin, Kevin Lin, Linjie Li +2
We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually new…
SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning
Kevin Lin, Linjie Li, Chung-Ching Lin +5
The canonical approach to video captioning dictates a caption generation model to learn from offline-extracted dense video features. These feature extractors usually operate on vid…
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Yue Meng, Rameswar Panda, Chung-Ching Lin +5
Temporal modelling is the key for efficient video action recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing tempor…
VA-RED: Video Adaptive Redundancy Reduction
Bowen Pan, Rameswar Panda, Camilo Fosco +6
Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition. An inherent p…
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Yue Meng, Chung-Ching Lin, Rameswar Panda +5
Action recognition is an open and challenging problem in computer vision. While current state-of-the-art models offer excellent recognition results, their computational expense lim…
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
Noel C. F. Codella, Chung-Ching Lin, Allan Halpern +3
Automated dermoscopic image analysis has witnessed rapid growth in diagnostic performance. Yet adoption faces resistance, in part, because no evidence is provided to support decisi…
Distributed Bundle Adjustment
Karthikeyan Natesan Ramamurthy, Chung-Ching Lin, Aleksandr Aravkin +2
Most methods for Bundle Adjustment (BA) in computer vision are either centralized or operate incrementally. This leads to poor scaling and affects the quality of solution as the nu…