1 citations · 2 across the 10 of their papers we have counts for
11 papers
Weave Forcing: Compositional Memory Routing for Interactive Long Video Generation
Ziyi Wang, Junchi Yao, Heqian Qiu +5
Recent advances in autoregressive video generation have improved temporal consistency over extended durations, yet interactive storytelling requires more than continuous scene exte…
Exo2EgoPose: Leveraging Exocentric Demonstrations for Vision-Language guided Egocentric 3D Hand Pose Forecasting
Zhaofeng Shi, Heqian Qiu, Lanxiao Wang +2
Perceiving multimodal cues and forecasting fine-grained actions from an egocentric (Ego) perspective is vital for applications like robot manipulation. However, previous studies ei…
SAVA-X: Ego-to-Exo Imitation Error Detection via Scene-Adaptive View Alignment and Bidirectional Cross View Fusion
Xiang Li, Heqian Qiu, Lanxiao Wang +4
Error detection is crucial in industrial training, healthcare, and assembly quality control. Most existing work assumes a single-view setting and cannot handle the practical case w…
EgoMe: A New Dataset and Challenge for Following Me via Egocentric View in Real World
Heqian Qiu, Zhaofeng Shi, Lanxiao Wang +3
In human imitation learning, the imitator typically take the egocentric view as a benchmark, naturally transferring behaviors observed from an exocentric view to their owns, which…
Modulating CNN Features with Pre-Trained ViT Representations for Open-Vocabulary Object Detection
Xiangyu Gao, Yu Dai, Benliu Qiu +3
Owing to large-scale image-text contrastive training, pre-trained vision language model (VLM) like CLIP shows superior open-vocabulary recognition ability. Most existing open-vocab…
Region Prompt Tuning: Fine-grained Scene Text Detection Utilizing Region Text Prompt
Xingtao Lin, Heqian Qiu, Lanxiao Wang +3
Recent advancements in prompt tuning have successfully adapted large-scale models like Contrastive Language-Image Pre-trained (CLIP) for downstream tasks such as scene text detecti…
Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding Confusion
Shaoxu Cheng, Kanglei Geng, Chiyuan He +7
Continual Learning (CL) aims to enable Deep Neural Networks (DNNs) to learn new data without forgetting previously learned knowledge. The key to achieving this goal is to avoid con…
Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning
Jian Jiao, Yu Dai, Hefei Mei +5
Recent video class-incremental learning usually excessively pursues the accuracy of the newly seen classes and relies on memory sets to mitigate catastrophic forgetting of the old…
MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning
Huiyu Xiong, Lanxiao Wang, Heqian Qiu +3
To address the problem of catastrophic forgetting due to the invisibility of old categories in sequential input, existing work based on relatively simple categorization tasks has m…
GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection
Hefei Mei, Taijin Zhao, Shiyuan Tang +5
Few-shot object detection (FSOD) aims to achieve object detection only using a few novel class training data. Most of the existing methods usually adopt a transfer-learning strateg…
Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration
Lei Wang, Qingbo Wu, Desen Yuan +4
Learning based image quality assessment (IQA) models have obtained impressive performance with the help of reliable subjective quality labels, where mean opinion score (MOS) is the…