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Found 49 papers, 29 papers with code
MLLM-DataEngine: An Iterative Refinement Approach for MLLM
Despite the great advance of Multimodal Large Language Models (MLLMs) in both instruction dataset building and benchmarking, the independence of training and evaluation makes current MLLMs hard to further improve their capability under the guidance of evaluation results with a relatively low human cost.
3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
3D vision-language grounding (3D-VL) is an emerging field that aims to connect the 3D physical world with natural language, which is crucial for achieving embodied intelligence.
Tiny LVLM-eHub: Early Multimodal Experiments with Bard
Secondly, it conducts an in-depth analysis of LVLMs' predictions using the ChatGPT Ensemble Evaluation (CEE), which leads to a robust and accurate evaluation and exhibits improved alignment with human evaluation compared to the word matching approach.
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning.
Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model
This paper presents Instruct2Act, a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks.
SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
In this paper, different from previous 2D DG works, we focus on the 3D DG problem and propose a Single-dataset Unified Generalization (SUG) framework that only leverages a single source dataset to alleviate the unforeseen domain differences faced by a well-trained source model.
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes.
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners
Our CaFo incorporates CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, DALL-E's vision-generative knowledge, and GPT-3's language-generative knowledge.
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning.
UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline
Different from previous studies that only focus on a single adaptation task, UniDA3D can tackle several adaptation tasks in 3D segmentation field, by designing a unified source-and-target active sampling strategy, which selects a maximally-informative subset from both source and target domains for effective model adaptation.
Full-Body Articulated Human-Object Interaction
By learning the geometrical relationships in HOI, we devise the very first model that leverage human pose estimation to tackle the estimation of articulated object poses and shapes during whole-body interactions.
Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation
Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding.
GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts
Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation.
HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes
Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes remains challenging due to the mediocre characteristics of the existing datasets on Human-Scene Interaction (HSI); they only have limited scale/quality and lack semantics.
Improving Object-centric Learning with Query Optimization
The ability to decompose complex natural scenes into meaningful object-centric abstractions lies at the core of human perception and reasoning.
SQA3D: Situated Question Answering in 3D Scenes
We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D).
Ranked #1 on
Referring Expression
on SQA3D
EgoTaskQA: Understanding Human Tasks in Egocentric Videos
The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i. e., state changes), and their causal dependencies.
Neural-Symbolic Recursive Machine for Systematic Generalization
In experiments, NSR achieves state-of-the-art performance in three benchmarks from different domains: SCAN for semantic parsing, PCFG for string manipulation, and HINT for arithmetic reasoning.
GenDexGrasp: Generalizable Dexterous Grasping
By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands.
Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World
We simulated the process from cloth to clothing in the digital world and then designed the adversarial "QR code" pattern.
Adversarial Texture for Fooling Person Detectors in the Physical World
Experiments showed that these clothes could fool person detectors in the physical world.
PartAfford: Part-level Affordance Discovery from 3D Objects
We propose a novel learning framework for PartAfford, which discovers part-level representations by leveraging only the affordance set supervision and geometric primitive regularization, without dense supervision.
Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers.
Infrared Invisible Clothing: Hiding From Infrared Detectors at Multiple Angles in Real World
We simulated the process from cloth to clothing in the digital world and then designed the adversarial "QR code" pattern.
YouRefIt: Embodied Reference Understanding with Language and Gesture
To the best of our knowledge, this is the first embodied reference dataset that allows us to study referring expressions in daily physical scenes to understand referential behavior, human communication, and human-robot interaction.
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc.
Ranked #4 on
3D Object Detection
on SUN-RGBD
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS).
Ranked #1 on
Mathematical Question Answering
on GeoS
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis
Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose.
VLGrammar: Grounded Grammar Induction of Vision and Language
In this work, we study grounded grammar induction of vision and language in a joint learning framework.
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
We believe the HINT dataset and the experimental findings are of great interest to the learning community on systematic generalization.
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability.
Learning by Fixing: Solving Math Word Problems with Weak Supervision
To generate more diverse solutions, \textit{tree regularization} is applied to guide the efficient shrinkage and exploration of the solution space, and a \textit{memory buffer} is designed to track and save the discovered various fixes for each problem.
Ranked #1 on
Math Word Problem Solving
on Math23K
(weakly-supervised metric)
LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities
Understanding and interpreting human actions is a long-standing challenge and a critical indicator of perception in artificial intelligence.
A Competence-aware Curriculum for Visual Concepts Learning via Question Answering
Humans can progressively learn visual concepts from easy to hard questions.
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
In this paper, we address these issues and close the loop of neural-symbolic learning by (1) introducing the \textbf{grammar} model as a \textit{symbolic prior} to bridge neural perception and symbolic reasoning, and (2) proposing a novel \textbf{back-search} algorithm which mimics the top-down human-like learning procedure to propagate the error through the symbolic reasoning module efficiently.
Memory-efficient training with streaming dimensionality reduction
The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads.
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.
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 #3 on
Monocular 3D Object Detection
on SUN RGB-D
Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning
This paper addresses a new problem of understanding human gaze communication in social videos from both atomic-level and event-level, which is significant for studying human social interactions.
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
Streaming Batch Eigenupdates for Hardware Neuromorphic Networks
Neuromorphic networks based on nanodevices, such as metal oxide memristors, phase change memories, and flash memory cells, have generated considerable interest for their increased energy efficiency and density in comparison to graphics processing units (GPUs) and central processing units (CPUs).
Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion
This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1).
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
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.
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 #6 on
Room Layout Estimation
on SUN RGB-D
Predicting Human Activities Using Stochastic Grammar
This paper presents a novel method to predict future human activities from partially observed RGB-D videos.
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.
Nonlinear Local Metric Learning for Person Re-identification
In this paper, we propose a nonlinear local metric learning (NLML) method to improve the state-of-the-art performance of person re-identification on public datasets.
Image Set Querying Based Localization
Conventional single image based localization methods usually fail to localize a querying image when there exist large variations between the querying image and the pre-built scene.
