Search Results for author:
Found 15 papers, 6 papers with code
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class Discovery
To counteract this inefficiency, we opt to cluster only the unlabelled instances and subsequently expand the cluster prototypes with our introduced potential prototypes to fast explore novel classes.
Towards Geometric-Photometric Joint Alignment for Facial Mesh Registration
This paper presents a Geometric-Photometric Joint Alignment(GPJA) method, for accurately aligning human expressions by combining geometry and photometric information.
Dual Relation Alignment for Composed Image Retrieval
Unlike the general image-text retrieval problem with only one alignment relation, i. e., image-text, we argue for the existence of two types of relations in composed image retrieval.
DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion
We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions while preserving motion diversity. Despite the recent significant process in text-based human motion generation, existing methods often prioritize fitting training motions at the expense of action diversity.
Ranked #2 on
Motion Synthesis
on HumanML3D
(using extra training data)
Knowledge Transfer-Driven Few-Shot Class-Incremental Learning
Concretely, RESA mimics the real incremental setting and constructs pseudo incremental tasks globally and locally, where the global pseudo incremental tasks are designed to coincide with the learning objective of FSCIL and the local pseudo incremental tasks are designed to improve the model's plasticity, respectively.
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark
To verify the feasibility of learning from the generated data, we develop a new joint Attribute Prompt Learning and Text Matching Learning (APTM) framework, considering the shared knowledge between attribute and text.
Ranked #1 on
Text based Person Retrieval
on CUHK-PEDES
Multimodal Learning for Non-small Cell Lung Cancer Prognosis
This paper focuses on the task of survival time analysis for lung cancer.
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning
The former is to reduce the memory cost by preserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensing stage.
Generating Superpixels for High-resolution Images with Decoupled Patch Calibration
Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning.
ReGO: Reference-Guided Outpainting for Scenery Image
We aim to tackle the challenging yet practical scenery image outpainting task in this work.
AINet: Association Implantation for Superpixel Segmentation
However, simply applying a series of convolution operations with limited receptive fields can only implicitly perceive the relations between the pixel and its surrounding grids.
DONet: Dual Objective Networks for Skin Lesion Segmentation
Skin lesion segmentation is a crucial step in the computer-aided diagnosis of dermoscopic images.
Sketch-Guided Scenery Image Outpainting
In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance.
Position Focused Attention Network for Image-Text Matching
Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence.
