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Found 14 papers, 4 papers with code
Generalized Max Pooling
Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics, such as the bag-of-visual-words (BOV) and its soft- and sparse-coding extensions.
Discovering beautiful attributes for aesthetic image analysis
We then describe how these three key components of AVA - images, scores, and comments - can be effectively leveraged to learn visual attributes.
LEWIS: Latent Embeddings for Word Images and their Semantics
The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images.
Interferences in match kernels
The second one involves equalising the match of a single descriptor to the aggregated vector.
A deep architecture for unified aesthetic prediction
Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories.
Re-ID done right: towards good practices for person re-identification
In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification.
End-to-End Saliency Mapping via Probability Distribution Prediction
Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection.
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models
With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for "Procedural Human Action Videos".
Virtual KITTI 2
This paper introduces an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark.
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
Ranked #1 on
Action Segmentation
on MPII Cooking 2 Dataset
Kernelized Few-Shot Object Detection With Efficient Integral Aggregation
We design a Kernelized Few-shot Object Detector by leveraging kernelized matrices computed over multiple proposal regions, which yield expressive non-linear representations whose model complexity is learned on the fly.
Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection
To address these drawbacks, we propose a Time-rEversed diffusioN tEnsor Transformer (TENET), which i) forms high-order tensor representations that capture multi-way feature occurrences that are highly discriminative, and ii) uses a transformer that dynamically extracts correlations between the query image and the entire support set, instead of a single average-pooled support embedding.
Dungeons and Data: A Large-Scale NetHack Dataset
Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets.
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation
For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels.
