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Found 23 papers, 12 papers with code
Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization
3D Visual Grounding (3DVG) and 3D Dense Captioning (3DDC) are two crucial tasks in various 3D applications, which require both shared and complementary information in localization and visual-language relationships.
X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation
Nevertheless, a substantial domain gap exists between 2D images and 3D assets, primarily attributed to variations in camera-related attributes and the exclusive presence of foreground objects.
Towards Omni-supervised Referring Expression Segmentation
To address this issue, we propose a new learning task for RES called Omni-supervised Referring Expression Segmentation (Omni-RES), which aims to make full use of unlabeled, fully labeled and weakly labeled data, e. g., referring points or grounding boxes, for efficient RES training.
Pseudo-label Alignment for Semi-supervised Instance Segmentation
Through extensive experiments conducted on the COCO and Cityscapes datasets, we demonstrate that PAIS is a promising framework for semi-supervised instance segmentation, particularly in cases where labeled data is severely limited.
Improving Human-Object Interaction Detection via Virtual Image Learning
Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects, which plays a curtail role in high-level semantic understanding tasks.
Approximated Prompt Tuning for Vision-Language Pre-trained Models
Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens.
InterFormer: Real-time Interactive Image Segmentation
Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks.
X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance
Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text.
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Then we derive two specific attention modules, named SpatialFormer Semantic Attention (SFSA) and SpatialFormer Target Attention (SFTA), to enhance the target object regions while reduce the background distraction.
Towards Efficient Visual Adaption via Structural Re-parameterization
Experimental results show the superior performance and efficiency of RepAdapter than the state-of-the-art PETL methods.
RefCLIP: A Universal Teacher for Weakly Supervised Referring Expression Comprehension
Based on RefCLIP, we further propose the first model-agnostic weakly supervised training scheme for existing REC models, where RefCLIP acts as a mature teacher to generate pseudo-labels for teaching common REC models.
Instance and Category Supervision are Alternate Learners for Continual Learning
Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills.
Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference
As a result, our dynamic inference network is trained independently of baseline and provides a flexible, efficient solution to distinguish between tasks.
RefTeacher: A Strong Baseline for Semi-Supervised Referring Expression Comprehension
In this paper, we present the first attempt of semi-supervised learning for REC and propose a strong baseline method called RefTeacher.
Category-aware Allocation Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects based on only image-level labels as supervision.
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection
Specifically, we first propose a normality recall memory (NR Memory) to strengthen the normality of student-generated features by recalling the stored normal information.
Ranked #11 on
Anomaly Detection
on MVTec AD
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning.
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification.
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances.
Ranked #3 on
Few-Shot Object Detection
on MS-COCO (1-shot)
Class-Aware Contrastive Semi-Supervised Learning
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
ISDNet: Integrating Shallow and Deep Networks for Efficient Ultra-High Resolution Segmentation
The huge burden of computation and memory are two obstacles in ultra-high resolution image segmentation.
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies.
OMPQ: Orthogonal Mixed Precision Quantization
Instead of solving a problem of the original integer programming, we propose to optimize a proxy metric, the concept of network orthogonality, which is highly correlated with the loss of the integer programming but also easy to optimize with linear programming.
