Data to 7 Oct 2026

Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts

arXiv 1406.2031 ↗First posted 8 Jun 2014cs.CVcited by 230

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Cited by 27 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of holistic object and body part combinations to better deal with different "detectability" patterns caused by deformations, occlusion and/or low resolution. We apply our method to the six animal categories in the PASCAL VOC dataset and show that our method significantly improves state-of-the-art (by 4.1% AP) and provides a richer representation for objects. During training we use annotations for body parts (e.g., head, torso, etc), making use of a new dataset of fully annotated object parts for PASCAL VOC 2010, which provides a mask for each part.

Key facts. Cited by 230 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2014.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 28; 2022: 28; 2023: 53; 2024: 45; 2025: 53; 2026: 23.

230 papers.

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2022Token Merging: Your ViT But Fastercited by 995
2023LISA: Reasoning Segmentation via Large Language Modelcited by 770
2021Deep ViT Features as Dense Visual Descriptorscited by 327
2023Semantic-SAM: Segment and Recognize Anything at Any Granularitycited by 185
2023Osprey: Pixel Understanding with Visual Instruction Tuningcited by 158
2023GSVA: Generalized Segmentation via Multimodal Large Language Modelscited by 153
2023PACO: Parts and Attributes of Common Objectscited by 146
2023Matcher: Segment Anything with One Shot Using All-Purpose Feature Matchingcited by 142
2022SemMAE: Semantic-Guided Masking for Learning Masked Autoencoderscited by 113
2021PartImageNet: A Large, High-Quality Dataset of Partscited by 107
2023DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Renderingcited by 103
2024PSALM: Pixelwise SegmentAtion with Large Multi-Modal Modelcited by 100

38 more

2024One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videoscited by 99
2022InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understandingcited by 89
2021Repurposing GANs for One-shot Semantic Part Segmentationcited by 88
2023Fine-Grained Visual Promptingcited by 81
2021Exploring Relational Context for Multi-Task Dense Predictioncited by 71
2024SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentationcited by 68
2022LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discoverycited by 65
2024EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Modelcited by 63
2023Concept Decomposition for Visual Exploration and Inspirationcited by 61
2022Factorizing Knowledge in Neural Networkscited by 59
2024Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detectioncited by 59
2024Multi-Task Dense Prediction via Mixture of Low-Rank Expertscited by 58
2021Unsupervised Part Discovery from Contrastive Reconstructioncited by 57
2021Part-aware Panoptic Segmentationcited by 56
2024Visual Agents as Fast and Slow Thinkerscited by 53
2022OPD: Single-view 3D Openable Part Detectioncited by 52
2023Going Denser with Open-Vocabulary Part Segmentationcited by 51
20244D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic Annotationscited by 51
2023PLIP: Language-Image Pre-training for Person Representation Learningcited by 49
2021OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Imagescited by 46
2023TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Expertscited by 45
2021Learning Multiple Dense Prediction Tasks from Partially Annotated Datacited by 42
2024Bridge the Points: Graph-based Few-shot Segment Anything Semanticallycited by 35
2022Multi-Task Learning with Multi-Query Transformer for Dense Predictioncited by 34
2023SLiMe: Segment Like Mecited by 34
2023Tracking with Human-Intent Reasoningcited by 32
2024DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perceptioncited by 32
2023OV-PARTS: Towards Open-Vocabulary Part Segmentationcited by 31
2021EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use casecited by 30
2022Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemblecited by 30
2025MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentationcited by 28
2023DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Predictioncited by 27
2025UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interfacecited by 27
2022Versatile Multi-Modal Pre-Training for Human-Centric Perceptioncited by 26
2025The Devil is in Temporal Token: High Quality Video Reasoning Segmentationcited by 26
2024CoReS: Orchestrating the Dance of Reasoning and Segmentationcited by 25
2023ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collectionscited by 22
2023InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene Understandingcited by 22

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