Search Results for author:
Found 29 papers, 12 papers with code
Propagating Over Phrase Relations for One-Stage Visual Grounding
Phrase level visual grounding aims to locate in an image the corresponding visual regions referred to by multiple noun phrases in a given sentence.
SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model
In this paper, we introduce the Sequential 3D Affordance Reasoning task, which extends the traditional paradigm by reasoning from cumbersome user intentions and then decomposing them into a series of segmentation maps.
Plain-Det: A Plain Multi-Dataset Object Detector
Recent advancements in large-scale foundational models have sparked widespread interest in training highly proficient large vision models.
Part2Object: Hierarchical Unsupervised 3D Instance Segmentation
By training Hi-Mask3D on the objects and object parts extracted from Part2Object, we achieve consistent and superior performance compared to state-of-the-art models in various settings, including unsupervised instance segmentation, data-efficient fine-tuning, and cross-dataset generalization.
The devil is in the object boundary: towards annotation-free instance segmentation using Foundation Models
Foundation models, pre-trained on a large amount of data have demonstrated impressive zero-shot capabilities in various downstream tasks.
Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation
Referring image segmentation (RIS) aims to precisely segment referents in images through corresponding natural language expressions, yet relying on cost-intensive mask annotations.
RealDex: Towards Human-like Grasping for Robotic Dexterous Hand
In this paper, we introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns, enriched by multi-view and multimodal visual data.
OMG: Towards Open-vocabulary Motion Generation via Mixture of Controllers
At the subsequent fine-tuning stage, we introduce motion ControlNet, which incorporates text prompts as conditioning information, through a trainable copy of the pre-trained model and the proposed novel Mixture-of-Controllers (MoC) block.
TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition
Furthermore, when stacking token mixers that consist of convolution and self-attention to form a deep network, the static nature of convolution hinders the fusion of features previously generated by self-attention into convolution kernels.
Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator
Text-to-video is a rapidly growing research area that aims to generate a semantic, identical, and temporal coherence sequence of frames that accurately align with the input text prompt.
Temporal Collection and Distribution for Referring Video Object Segmentation
Furthermore, to explicitly capture object motions and spatial-temporal cross-modal reasoning over objects, we propose a novel temporal collection-distribution mechanism for interacting between the global referent token and object queries.
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection
Its challenge lies in object categories available for the task being too diverse to be limited to a closed set of object vocabulary for traditional object detection.
LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models
Prompt engineering is a powerful tool used to enhance the performance of pre-trained models on downstream tasks.
EdaDet: Open-Vocabulary Object Detection Using Early Dense Alignment
Vision-language models such as CLIP have boosted the performance of open-vocabulary object detection, where the detector is trained on base categories but required to detect novel categories.
Contrastive Grouping with Transformer for Referring Image Segmentation
Referring image segmentation aims to segment the target referent in an image conditioning on a natural language expression.
Grounded Image Text Matching with Mismatched Relation Reasoning
This paper introduces Grounded Image Text Matching with Mismatched Relation (GITM-MR), a novel visual-linguistic joint task that evaluates the relation understanding capabilities of transformer-based pre-trained models.
WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural Language
We introduce the task of 3D visual grounding in large-scale dynamic scenes based on natural linguistic descriptions and online captured multi-modal visual data, including 2D images and 3D LiDAR point clouds.
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views.
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e. g.,} social network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations.
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?
Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets.
Bottom-Up Shift and Reasoning for Referring Image Segmentation
In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules.
Graph-Structured Referring Expression Reasoning in The Wild
The linguistic structure of a referring expression provides a layout of reasoning over the visual contents, and it is often crucial to align and jointly understand the image and the referring expression.
Dynamic Graph Attention for Referring Expression Comprehension
In this paper, we explore the problem of referring expression comprehension from the perspective of language-driven visual reasoning, and propose a dynamic graph attention network to perform multi-step reasoning by modeling both the relationships among the objects in the image and the linguistic structure of the expression.
Relationship-Embedded Representation Learning for Grounding Referring Expressions
Unfortunately, existing work on grounding referring expressions fails to accurately extract multi-order relationships from the referring expression and associate them with the objects and their related contexts in the image.
Cross-Modal Relationship Inference for Grounding Referring Expressions
A feasible solution for grounding referring expressions not only needs to extract all the necessary information (i. e. objects and the relationships among them) in both the image and referring expressions, but also compute and represent multimodal contexts from the extracted information.
Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks
In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against adversarial attacks.
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning
In this paper, we propose a novel weakly supervised curriculum learning pipeline for multi-label object recognition, detection and semantic segmentation.
Ranked #21 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Introduction to Clustering Algorithms and Applications
Data clustering is the process of identifying natural groupings or clusters within multidimensional data based on some similarity measure.
