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Found 13 papers, 8 papers with code
MoReVQA: Exploring Modular Reasoning Models for Video Question Answering
This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage, modular reasoning framework.
Efficient Semantic Matching with Hypercolumn Correlation
Recent studies show that leveraging the match-wise relationships within the 4D correlation map yields significant improvements in establishing semantic correspondences - but at the cost of increased computation and latency.
Peripheral Vision Transformer
We propose to incorporate peripheral position encoding to the multi-head self-attention layers to let the network learn to partition the visual field into diverse peripheral regions given training data.
TransforMatcher: Match-to-Match Attention for Semantic Correspondence
Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations.
Ranked #10 on
Semantic correspondence
on SPair-71k
Visual TransforMatcher: Efficient Match-to-Match Attention for Visual Correspondence
Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints and intra-class variations.
Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence
To validate the proposed techniques, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling.
Relational Embedding for Few-Shot Classification
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective.
Ranked #15 on
Few-Shot Image Classification
on CUB 200 5-way 5-shot
Hypercorrelation Squeeze for Few-Shot Segmentation
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class.
Ranked #13 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
Convolutional Hough Matching Networks
Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images.
Ranked #4 on
Semantic correspondence
on PF-WILLOW
Hypercorrelation Squeeze for Few-Shot Segmenation
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class.
Learning to Compose Hypercolumns for Visual Correspondence
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers.
Ranked #2 on
Semantic correspondence
on Caltech-101
SPair-71k: A Large-scale Benchmark for Semantic Correspondence
In this paper, we present a new large-scale benchmark dataset of semantically paired images, SPair-71k, which contains 70, 958 image pairs with diverse variations in viewpoint and scale.
Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features
Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details.
Ranked #1 on
Semantic correspondence
on Caltech-101
