21 citations · 51 across the 9 of their papers we have counts for
9 papers
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision
Ruiyan Han, Zhen Fang, XinYu Sun +9
While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledg…
Neural Randomized Planning for Whole Body Robot Motion
Yunfan Lu, Yuchen Ma, David Hsu +1
Robot motion planning has made vast advances over the past decades, but the challenge remains: robot mobile manipulators struggle to plan long-range whole-body motion in common hou…
DiT: Efficient Vision Transformers with Dynamic Token Routing
Yuchen Ma, Zhengcong Fei, Junshi Huang
Recently, the tokens of images share the same static data flow in many dense networks. However, challenges arise from the variance among the objects in images, such as large variat…
Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment
Yuchen Ma, Yanbei Chen, Zeynep Akata
Recent advances have indicated the strengths of self-supervised pre-training for improving representation learning on downstream tasks. Existing works often utilize self-supervised…
Generalized Few-Shot Object Detection without Forgetting
Zhibo Fan, Yuchen Ma, Zeming Li +1
Recently few-shot object detection is widely adopted to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we clai…
IQDet: Instance-wise Quality Distribution Sampling for Object Detection
Yuchen Ma, Songtao Liu, Zeming Li +1
We propose a dense object detector with an instance-wise sampling strategy, named IQDet. Instead of using human prior sampling strategies, we first extract the regional feature of…
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track
Zeming Li, Yuchen Ma, Yukang Chen +2
In this report, we present our object detection/instance segmentation system, MegDetV2, which works in a two-pass fashion, first to detect instances then to obtain segmentation. Ou…
BorderDet: Border Feature for Dense Object Detection
Han Qiu, Yuchen Ma, Zeming Li +2
Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted t…
A Novel Artificial Fish Swarm Algorithm for Pattern Recognition with Convex Optimization
Lei Shi, Rui Guo, Yuchen Ma
Image pattern recognition is an important area in digital image processing. An efficient pattern recognition algorithm should be able to provide correct recognition at a reduced co…