Search Results for author:
Found 12 papers, 9 papers with code
Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence
In this work, we first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes.
Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs
This work delves into the task of pose-free novel view synthesis from stereo pairs, a challenging and pioneering task in 3D vision.
DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation
In the paradigm of AI-generated content (AIGC), there has been increasing attention to transferring knowledge from pre-trained text-to-image (T2I) models to text-to-video (T2V) generation.
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions.
Ranked #1 on
Open Vocabulary Semantic Segmentation
on ADE20K-150
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters.
Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence
The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggregation.
Ranked #1 on
Geometric Matching
on HPatches
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
However, the tokenization of a correlation map for transformer processing can be detrimental, because the discontinuity at token boundaries reduces the local context available near the token edges and decreases inductive bias.
Ranked #2 on
Semantic correspondence
on PF-WILLOW
CATs++: Boosting Cost Aggregation with Convolutions and Transformers
Cost aggregation is a highly important process in image matching tasks, which aims to disambiguate the noisy matching scores.
Ranked #3 on
Semantic correspondence
on PF-PASCAL
Cost Aggregation Is All You Need for Few-Shot Segmentation
We introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support.
Ranked #3 on
Semantic correspondence
on PF-WILLOW
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation
Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest.
Deep Matching Prior: Test-Time Optimization for Dense Correspondence
Conventional techniques to establish dense correspondences across visually or semantically similar images focused on designing a task-specific matching prior, which is difficult to model.
Ranked #1 on
Dense Pixel Correspondence Estimation
on HPatches
(using extra training data)
CATs: Cost Aggregation Transformers for Visual Correspondence
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations.
Ranked #5 on
Semantic correspondence
on PF-WILLOW
