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Found 31 papers, 10 papers with code
SketchINR: A First Look into Sketches as Implicit Neural Representations
(ii) SketchINR's auto-decoder provides a much higher-fidelity representation than other learned vector sketch representations, and is uniquely able to scale to complex vector sketches such as FS-COCO.
What Sketch Explainability Really Means for Downstream Tasks
In this paper, we explore the unique modality of sketch for explainability, emphasising the profound impact of human strokes compared to conventional pixel-oriented studies.
You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval
Two primary input modalities prevail in image retrieval: sketch and text.
It's All About Your Sketch: Democratising Sketch Control in Diffusion Models
This paper unravels the potential of sketches for diffusion models, addressing the deceptive promise of direct sketch control in generative AI.
Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers
This paper, for the first time, explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR).
How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?
@q loss to inject that understanding into the system.
Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes
In this paper, we democratise 3D content creation, enabling precise generation of 3D shapes from abstract sketches while overcoming limitations tied to drawing skills.
DemoCaricature: Democratising Caricature Generation with a Rough Sketch
In this paper, we democratise caricature generation, empowering individuals to effortlessly craft personalised caricatures with just a photo and a conceptual sketch.
3D VR Sketch Guided 3D Shape Prototyping and Exploration
3D shape modeling is labor-intensive, time-consuming, and requires years of expertise.
What Can Human Sketches Do for Object Detection?
In particular, we first perform independent prompting on both sketch and photo branches of an SBIR model to build highly generalisable sketch and photo encoders on the back of the generalisation ability of CLIP.
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%.
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not
At the very core of our solution is a prompt learning setup.
Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings
Human sketch has already proved its worth in various visual understanding tasks (e. g., retrieval, segmentation, image-captioning, etc).
Picture that Sketch: Photorealistic Image Generation from Abstract Sketches
We further introduce specific designs to tackle the abstract nature of human sketches, including a fine-grained discriminative loss on the back of a trained sketch-photo retrieval model, and a partial-aware sketch augmentation strategy.
Democratising 2D Sketch to 3D Shape Retrieval Through Pivoting
We perform pivoting on two existing datasets, each from a distant research domain to the other: 2D sketch and photo pairs from the sketch-based image retrieval field (SBIR), and 3D shapes from ShapeNet.
Adaptive Fine-Grained Sketch-Based Image Retrieval
To solve this new problem, we introduce a novel model-agnostic meta-learning (MAML) based framework with several key modifications: (1) As a retrieval task with a margin-based contrastive loss, we simplify the MAML training in the inner loop to make it more stable and tractable.
SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text
In this paper, we extend scene understanding to include that of human sketch.
Sketch3T: Test-Time Training for Zero-Shot SBIR
In this paper, we question to argue that this setup by definition is not compatible with the inherent abstract and subjective nature of sketches, i. e., the model might transfer well to new categories, but will not understand sketches existing in different test-time distribution as a result.
Partially Does It: Towards Scene-Level FG-SBIR with Partial Input
We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial".
Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval
We first conducted a pilot study that revealed the secret lies in the existence of noisy strokes, but not so much of the "I can't sketch".
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context
We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO.
SketchLattice: Latticed Representation for Sketch Manipulation
The key challenge in designing a sketch representation lies with handling the abstract and iconic nature of sketches.
Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation
In this paper, for the first time, we argue for their unification -- we aim for a single model that can compete favourably with two separate state-of-the-art STR and HTR models.
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition
In this paper, we argue that semantic information offers a complementary role in addition to visual only.
Towards the Unseen: Iterative Text Recognition by Distilling from Errors
Our framework is iterative in nature, in that it utilises predicted knowledge of character sequences from a previous iteration, to augment the main network in improving the next prediction.
MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition
In this paper, we take a completely different perspective -- we work on the assumption that there is always a new style that is drastically different, and that we will only have very limited data during testing to perform adaptation.
Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting
This data is uniquely characterised by its existence in dual modalities of rasterized images and vector coordinate sequences.
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval
A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) models is the data scarcity -- model performances are largely bottlenecked by the lack of sketch-photo pairs.
UDBNET: Unsupervised Document Binarization Network via Adversarial Game
In this paper, we present a novel approach towards document image binarization by introducing three-player min-max adversarial game.
Ranked #2 on
Binarization
on DIBCO 2011
Modeling Extent-of-Texture Information for Ground Terrain Recognition
Ground Terrain Recognition is a difficult task as the context information varies significantly over the regions of a ground terrain image.
ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense.
Cultural Vocal Bursts Intensity Prediction
General Classification
+2
