Search Results for author:
Found 22 papers, 11 papers with code
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars
We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms.
Predicting Human Activities Using Stochastic Grammar
This paper presents a novel method to predict future human activities from partially observed RGB-D videos.
Intent-aware Multi-agent Reinforcement Learning
We experiment our algorithm in a real-world problem that is non-episodic, and the number of agents and goals can vary over time.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction
Future predictions on sequence data (e. g., videos or audios) require the algorithms to capture non-Markovian and compositional properties of high-level semantics.
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model.
Ranked #4 on
Monocular 3D Object Detection
on SUN RGB-D
(AP@0.15 (10 / PNet-30) metric)
Learning Human-Object Interactions by Graph Parsing Neural Networks
For a given scene, GPNN infers a parse graph that includes i) the HOI graph structure represented by an adjacency matrix, and ii) the node labels.
Ranked #32 on
Human-Object Interaction Detection
on V-COCO
Human-centric Indoor Scene Synthesis Using Stochastic Grammar
We present a human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with perfect per-pixel ground truth.
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
Holistic 3D indoor scene understanding refers to jointly recovering the i) object bounding boxes, ii) room layout, and iii) camera pose, all in 3D.
Ranked #5 on
Monocular 3D Object Detection
on SUN RGB-D
Reasoning Visual Dialogs with Structural and Partial Observations
The answer to a given question is represented by a node with missing value.
Ranked #14 on
Visual Dialog
on VisDial v0.9 val
Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense
We propose a new 3D holistic++ scene understanding problem, which jointly tackles two tasks from a single-view image: (i) holistic scene parsing and reconstruction---3D estimations of object bounding boxes, camera pose, and room layout, and (ii) 3D human pose estimation.
3D Human Pose Estimation
Human-Object Interaction Detection
+1
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning
Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment.
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points
Detecting 3D objects from a single RGB image is intrinsically ambiguous, thus requiring appropriate prior knowledge and intermediate representations as constraints to reduce the uncertainties and improve the consistencies between the 2D image plane and the 3D world coordinate.
Ranked #2 on
Monocular 3D Object Detection
on SUN RGB-D
(AP@0.15 (10 / PNet-30) metric)
Learning Compositional Neural Information Fusion for Human Parsing
The bottom-up and top-down inferences explicitly model the compositional and decompositional relations in human bodies, respectively.
Cascaded Human-Object Interaction Recognition
The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction.
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.
Heterogeneous Value Alignment Evaluation for Large Language Models
We conduct evaluations with new auto-metric \textit{value rationality} to represent the ability of LLMs to align with specific values.
E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning
Specifically, we introduce a set of learnable key-value prompts and visual prompts into self-attention and input layers, respectively, to improve the effectiveness of model fine-tuning.
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers.
Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation
This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its superior performance compared to traditional full-finetuning.
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Our work marks a step forward in effectively and efficiently aligning models to diverse and intricate human preferences in a controllable and Pareto-optimal manner.
