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Found 34 papers, 16 papers with code
Lookahead Exploration with Neural Radiance Representation for Continuous Vision-Language Navigation
Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments.
Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection
The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories.
Vision-based Food Nutrition Estimation via RGB-D Fusion Network
In this paper, we designed one RGB-D fusion network, which integrated multimodal feature fusion (MMFF) and multi-scale fusion for visioin-based nutrition assessment.
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
To tackle this, we propose the Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food Detection (ZSFD).
Ranked #1 on
Generalized Zero-Shot Object Detection
on MS-COCO
Towards Food Image Retrieval via Generalization-oriented Sampling and Loss Function Design
Therefore, it is challenging to study the generalization of the model in food image retrieval.
GridMM: Grid Memory Map for Vision-and-Language Navigation
Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments.
Vision-Based Fruit Recognition via Multi-Scale Attention CNN
Fruit quality assessment, grading and sorting are of vital importance to fruit processing, and all these involve fruit recognition.
KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation
The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction.
Bi-Level Meta-Learning for Few-Shot Domain Generalization
We address FSDG problem by meta-learning two levels of meta-knowledge, where the lower-level meta-knowledge are domain-specific embedding spaces as subspaces of a base space for intra-domain generalization, and the upper-level meta-knowledge is the base space and a prior subspace over domain-specific spaces for inter-domain generalization.
Data-Free Knowledge Distillation via Feature Exchange and Activation Region Constraint
Therefore, we propose mSARC to assure the student network can imitate not only the logit output but also the spatial activation region of the teacher network in order to alleviate the influence of unwanted noises in diverse synthetic images on distillation learning.
Layout-Based Causal Inference for Object Navigation
The experience performs a positive effect on helping the agent infer the likely location of the goal when the layout gap between the unseen environments of the test and the prior knowledge obtained in training is minor.
Deep Learning for Logo Detection: A Survey
When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks.
See More for Scene: Pairwise Consistency Learning for Scene Classification
In this paper, we propose to understand scene images and the scene classification CNN models in terms of the focus area.
Hierarchical Object-to-Zone Graph for Object Navigation
In this paper, we propose a hierarchical object-to-zone (HOZ) graph to guide the agent in a coarse-to-fine manner, and an online-learning mechanism is also proposed to update HOZ according to the real-time observation in new environments.
Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection
Unlike general object detection, logo detection is a challenging task, especially for small logo objects and large aspect ratio logo objects in the real-world scenario.
FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network
For that, we propose a novel food logo detection method Multi-scale Feature Decoupling Network (MFDNet), which decouples classification and regression into two branches and focuses on the classification branch to solve the problem of distinguishing multiple food logo categories.
A review on vision-based analysis for automatic dietary assessment
We also provide the latest ideas for future development of VBDA, e. g., fine-grained food analysis and accurate volume estimation.
Applications of knowledge graphs for food science and industry
The deployment of various networks (e. g., Internet of Things [IoT] and mobile networks), databases (e. g., nutrition tables and food compositional databases), and social media (e. g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods.
Large Scale Visual Food Recognition
Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e. g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks.
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
Limited by the definition of AP, such methods consider both negative and positive instances ranking before each positive instance.
Ranked #3 on
Vehicle Re-Identification
on VehicleID Large
Dataset Bias in Few-shot Image Recognition
Second, we investigate performance differences on different datasets from dataset structures and different few-shot learning methods.
ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network
To encourage further progress in food recognition, we introduce the dataset ISIA Food- 500 with 500 categories from the list in the Wikipedia and 399, 726 images, a more comprehensive food dataset that surpasses existing popular benchmark datasets by category coverage and data volume.
LogoDet-3K: A Large-Scale Image Dataset for Logo Detection
LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets.
Ranked #1 on
Object Detection
on FlickrLogos-32
Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification
Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification.
Scene Recognition with Prototype-agnostic Scene Layout
Due to the large intra-class structural diversity, building and modeling flexible structural layout to adapt various image characteristics is a challenge.
Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition
In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition.
Food Recommendation: Framework, Existing Solutions and Challenges
A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e. g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat.
Learning Effective RGB-D Representations for Scene Recognition
We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scene.
A Survey on Food Computing
This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.
Computers and Society Multimedia
Food recognition and recipe analysis: integrating visual content, context and external knowledge
The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information.
Scene recognition with CNNs: objects, scales and dataset bias
Thus, adapting the feature extractor to each particular scale (i. e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales.
Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs
However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features.
Joint Multi-Feature Spatial Context for Scene Recognition on the Semantic Manifold
An important advantage of modeling features in a semantic space, is that this space is feature independent.
Multi-level Discriminative Dictionary Learning towards Hierarchical Visual Categorization
For each internode of the hierarchical category structure, a discriminative dictionary and a set of classification models are learnt for visual categorization, and the dictionaries in different layers are learnt to exploit the discriminative visual properties of different granularity.
