110 citations · 188 across the 14 of their papers we have counts for
14 papers
Hybrid Generative-Discriminative Object Placement
Siyuan Zhou, Li Niu
As an important operation of image composition, object placement aims to predict the plausible placement (location, scale) for the inserted foreground object. Previous object place…
Video Object of Interest Segmentation
Siyuan Zhou, Chunru Zhan, Biao Wang +3
In this work, we present a new computer vision task named video object of interest segmentation (VOIS). Given a video and a target image of interest, our objective is to simultaneo…
Weak-shot Semantic Segmentation via Dual Similarity Transfer
Junjie Chen, Li Niu, Siyuan Zhou +3
Semantic segmentation is an important and prevalent task, but severely suffers from the high cost of pixel-level annotations when extending to more classes in wider applications. T…
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning
Kefan Su, Siyuan Zhou, Jiechuan Jiang +3
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL). However, non-stationarity remains a significant challenge in fully decentr…
Learning Object Placement via Dual-path Graph Completion
Siyuan Zhou, Liu Liu, Li Niu +1
Object placement aims to place a foreground object over a background image with a suitable location and size. In this work, we treat object placement as a graph completion problem…
Finding Fallen Objects Via Asynchronous Audio-Visual Integration
Chuang Gan, Yi Gu, Siyuan Zhou +7
The way an object looks and sounds provide complementary reflections of its physical properties. In many settings cues from vision and audition arrive asynchronously but must be in…
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
Shusheng Xu, Yichen Liu, Xiaoyu Yi +3
We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese. NCR is collected fro…
Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary
Siyuan Zhou, Li Niu, Jianlou Si +2
Weakly-supervised semantic segmentation (WSSS) with image-level labels has been widely studied to relieve the annotation burden of the traditional segmentation task. In this paper,…
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
Zhiao Huang, Yuanming Hu, Tao Du +4
Simulated virtual environments serve as one of the main driving forces behind developing and evaluating skill learning algorithms. However, existing environments typically only sim…
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
Chuang Gan, Siyuan Zhou, Jeremy Schwartz +8
We introduce a visually-guided and physics-driven task-and-motion planning benchmark, which we call the ThreeDWorld Transport Challenge. In this challenge, an embodied agent equipp…
Learning Task Decomposition with Ordered Memory Policy Network
Yuchen Lu, Yikang Shen, Siyuan Zhou +3
Many complex real-world tasks are composed of several levels of sub-tasks. Humans leverage these hierarchical structures to accelerate the learning process and achieve better gener…
From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic Segmentation
Zhangxuan Gu, Siyuan Zhou, Li Niu +2
Zero-shot learning has been actively studied for image classification task to relieve the burden of annotating image labels. Interestingly, semantic segmentation task requires more…
Context-aware Feature Generation for Zero-shot Semantic Segmentation
Zhangxuan Gu, Siyuan Zhou, Li Niu +2
Existing semantic segmentation models heavily rely on dense pixel-wise annotations. To reduce the annotation pressure, we focus on a challenging task named zero-shot semantic segme…
Transferable Interactiveness Knowledge for Human-Object Interaction Detection
Yong-Lu Li, Siyuan Zhou, Xijie Huang +5
Human-Object Interaction (HOI) Detection is an important problem to understand how humans interact with objects. In this paper, we explore Interactiveness Knowledge which indicates…