Analyzing the history of CVPR
As the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025 approaches, let’s take a look at the history of the conference and its workshops from 2017 to 2024. The goal of this analysis is to provide insights into the evolution of topics and trends in artificial intelligence research over the years. Keep in mind that this information should be taken with a grain of salt, as some of the information that may be relevant to the analyses is discarded during the cleaning process. Some of the analysis is based on keywords, and we make some assumptions about how authors use keywords (e.g. it’s pretty unlikely that a paper about image data would have the keyword audio in its title or abstract), but this is not a perfect solution. The goal of this post is to give some insight into the history of the conference, not to be a definitive analysis.
Note that some of the graphs use percentiles of the total number of papers published in each year. Since there are different numbers of papers published each year, you can’t really compare the numbers from one year to the next. The goal of these graphs is to show the distribution of papers published during the period and any changes in the focus of the academic community. You can also interact with the visualizations here. You can zoom in on specific parts, enable or disable lines by clicking on their names in the legend, and hover over the points to see more information.
Overall Statistics
Here you can see the number of published papers. Each year, there are more and more papers published compared to the previous year, except for 2023. There were more than three times as many papers published in 2024 as in 2017.
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Regarding the modalities used in the papers, we can see that image is still the most common, but the use of text and multiple modalities has increased significantly. The application of optical flow, graphs and depth information has decreased in the last years, while the use of particles has remained relatively stable.
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It is quite common for papers to introduce new concepts, be it a new method, a new dataset, or a new architecture. The following graph shows the most common concepts introduced in the papers. Not surprisingly, algorithms are the most common concept. Algorithms also involve new methods or approaches. Novel tasks have also been introduced over the years, which is highly correlated with the creation of novel datasets. The introduction of new architectures has also increased in the last year, including new models, modules, and networks. The creation of different losses and metrics has been quite stable over the years, with very few papers introducing new ones.
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Regarding the common tasks in the papers, we can see a steep increase in generation tasks, especially after 2022. This may be related to the advances in large language models such as InstructGPT and ChatGPT by the end of 2022, and the release of the first collections of foundational language models such as LLaMA in early 2023. Classification, detection, estimation, and recognition have seen a decline in interest over the years, while prediction has only recently seen a decrease. Tasks such as segmentation have remained relatively stable. The use of reasoning tasks has also increased significantly in the last year, but is still a small percentage of the total number of published papers (about 3%).
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["recognition"], ["recognition"], ["recognition"]], "hovertemplate": "task=%{customdata[0]}<br>year=%{x}<br>occurrences (%)=%{y:.3f}<extra></extra>", "legendgroup": "recognition", "line": {"dash": "solid"}, "marker": {"symbol": "circle"}, "mode": "lines+markers", "name": "recognition", "orientation": "v", "showlegend": true, "x": {"dtype": "i2", "bdata": "4QfiB+MH5AflB+YH5wfoBw=="}, "xaxis": "x", "y": {"dtype": "f8", "bdata": "2FBeQ3kNJUCmpaWlpaUhQCbSA5WzWxxAumCS0WebG0D4XU+RqdAWQPSPD4zsUBhAQgNjKDJbFEBlCWzaTxYRQA=="}, "yaxis": "y", "type": "scatter"}, {"customdata": [["regression"], ["regression"], ["regression"], ["regression"], ["regression"], ["regression"], ["regression"], ["regression"]], "hovertemplate": "task=%{customdata[0]}<br>year=%{x}<br>occurrences (%)=%{y:.3f}<extra></extra>", "legendgroup": "regression", "line": {"dash": "solid"}, "marker": {"symbol": "circle"}, "mode": "lines+markers", "name": "regression", "orientation": "v", "showlegend": true, "x": {"dtype": "i2", 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["segmentation"]], "hovertemplate": "task=%{customdata[0]}<br>year=%{x}<br>occurrences (%)=%{y:.3f}<extra></extra>", "legendgroup": "segmentation", "line": {"dash": "solid"}, "marker": {"symbol": "circle"}, "mode": "lines+markers", "name": "segmentation", "orientation": "v", "showlegend": true, "x": {"dtype": "i2", "bdata": "4QfiB+MH5AflB+YH5wfoBw=="}, "xaxis": "x", "y": {"dtype": "f8", "bdata": "MHvA7AGzHUDRleTQleQgQAOPEmaxTyBAgF3rnygFIECrl4Tzis4dQF5PyhwDHCBAmwIc7fRIIEDPSMXc6swgQA=="}, "yaxis": "y", "type": "scatter"}, {"customdata": [["tracking"], ["tracking"], ["tracking"], ["tracking"], ["tracking"], ["tracking"], ["tracking"], ["tracking"]], "hovertemplate": "task=%{customdata[0]}<br>year=%{x}<br>occurrences (%)=%{y:.3f}<extra></extra>", "legendgroup": "tracking", "line": {"dash": "solid"}, "marker": {"symbol": "circle"}, "mode": "lines+markers", "name": "tracking", "orientation": "v", "showlegend": true, "x": {"dtype": "i2", "bdata": "4QfiB+MH5AflB+YH5wfoBw=="}, "xaxis": "x", 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"task=%{customdata[0]}<br>year=%{x}<br>occurrences (%)=%{y:.3f}<extra></extra>", "legendgroup": "verification", "line": {"dash": "solid"}, "marker": {"symbol": "circle"}, "mode": "lines+markers", "name": "verification", "orientation": "v", "showlegend": true, "x": {"dtype": "i2", "bdata": "4QfiB+MH5AflB+YH5wfoBw=="}, "xaxis": "x", "y": {"dtype": "f8", "bdata": "FbFUxFIR6z8dk/Uck/XsPxTvIsAgtO4/g0lGn20u6D+EcWIiEo3XP/SPD4zsUNg/eQPQ5pu21T84iB7fhYPQPw=="}, "yaxis": "y", "type": "scatter"}], "layout": {"xaxis": {"anchor": "y", "domain": [0.0, 1.0], "title": {"text": "year"}}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "title": {"text": "occurrences (%)"}}, "legend": {"title": {"text": "task"}, "tracegroupgap": 0}}}
Let’s dive a little deeper into the tasks.
Algorithms focused on security and privacy have been around for a while, but the number of papers published on them has increased significantly in the last year. Spoofing detection is crucial for applications such as identity recognition, where attackers may try to use photos or videos to impersonate someone else, and has seemed to gain urgency since deepfake technologies have become more prevalent.
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Explainability and interpretability has gained traction in the last few years, with a significant increase in the number of papers published on the topic around 2019, following a surge in some specific conferences and workshops on model transparency, interpretability, and fairness, such as ACM FaccT and VISxAI. Explainability is crucial for building trust in AI systems and ensuring that they make decisions based on valid reasoning. One of the areas that has seen the most investment in recent years is model grounding, the process of tying the model’s predictions to specific features in the input data. This is particularly important in applications such as image classification and question answering, where it is essential to understand which parts of an input (text, image) are driving the model’s predictions.
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Visual tasks such as image denoising have received a lot of attention in recent years, with many papers published on the topic. This may be due to the increasing importance of image quality in computer vision applications, the development of new techniques to improve image quality, and the increased capacity of visual models to handle larger inputs. This category of tasks also includes deblurring, dehazing, demoireing, deraining, and others. Image processing and image generation tasks have also increased significantly.
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Language tasks have also seen a fluctuation in the number of papers published over the past few years, particularly those that focus on dialogue and conversation. By using a conversational interface, users can interact with AI systems in a more natural and intuitive way, leading to better user experiences and more effective communication. This has led to a surge in research on dialog systems, including chatbots, virtual assistants, and other conversational agents. The development of large-scale language models has also played a significant role in this trend, as these models have demonstrated impressive capabilities in generating human-like text and understanding context.
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Multimodal tasks are one of the current trends in artificial intelligence. These tasks involve the combination of different modalities, such as audio, text, and images, to improve the performance of models and to solve problems that require a deeper understanding of the intermodality of the world. The number of papers published on these tasks has increased significantly in recent years, with a particular focus on tasks such as image-text alignment, image synthesis, video synthesis, and visual question answering. This trend is likely to continue as researchers explore new ways to combine different modalities in novel ways and improve the performance of models.
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Here we focus on analyzing the use of some keywords in the LLM papers. More specifically:
- Chain-of-Thought, Tree-of-Thought, and any of-Thought variations - these are prompting techniques that help the model to break down complex tasks into smaller, more manageable steps, allowing it to reason through the problem more effectively;
- Agent - refers to the use of LLMs as agents that can perform tasks autonomously, often in conjunction with other tools or systems;
- Distillation - a technique used to compress large models into smaller, more efficient ones while retaining their performance;
- Few-shot prompting - a prompting technique that provides the model with a few examples of the task at hand, allowing it to generalize and perform well on similar tasks;
- Fine-tuning - the process of training a pre-trained model on a specific task or dataset to improve its performance;
- Reinforcement Learning (RL) - a type of machine learning where an agent learns to make decisions by receiving feedback from its environment in the form of rewards or penalties;
- Retrieval Augmented Generation (RAG) - a technique that combines retrieval-based methods with generative models to improve the performance of language models on specific tasks;
- Self-Instruct - a technique that allows models to learn from their own outputs, improving their performance over time;
- Tokenizer - a component of language models that converts text into a format that the model can understand, often by breaking it down into smaller units called tokens;
- Tool - refers to the use of external tools or systems in conjunction with LLMs to perform tasks more effectively;
- Zero-shot prompting - a prompting technique that allows the model to perform tasks without any prior examples or training on that specific task.
Few-shot and zero-shot prompting have lost the interest of the academic community in favor of RAG, thought processes, and novel fine-tuning techniques. Interest in creating LLM agents that can tackle harder tasks and use tools is one of the hottest topics in the field.
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Information about Authors
Now let’s look at the authors of the papers. This first graph shows the number of papers published by each author. As we can see, most authors have published only one paper at the conference. Out of 33,861 authors, only 1,308 have 10 or more accepted papers.
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Here are the top 10 authors with the most papers:
| Author | Papers |
|---|---|
| Luc Van Gool | 134 |
| Radu Timofte | 119 |
| Lei Zhang | 100 |
| Yi Yang | 86 |
| Yu Qiao | 83 |
| Dacheng Tao | 80 |
| Ming-Hsuan Yang | 79 |
| Qi Tian | 75 |
| Marc Pollefeys | 71 |
| Xiaogang Wang | 70 |
Now let’s look at the number of authors per paper. Most of the papers have between 2 and 7 authors, but there are a few with a large number of authors, such as Why Is the Winner the Best?, which has 125 authors, and The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report, with a staggering 134 authors. The former is a multi-center study of all 80 competitions held as part of IEEE ISBI 2021 and MICCAI 2021, while the latter is a report summarizing the results of the NTIRE 2024 challenge, a competition held at the CVPR conference.
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Since most papers have multiple authors, it is quite common to see some authors constantly collaborating with each other. The most common pair of authors is Jiwen Lu and Jie Zhou, who have collaborated on 57 papers together. The second most common pair is Luc Van Gool and Radu Timofte with 43 papers together, followed by Tao Xiang and Yi-Zhe Song with 38 papers. The top 10 most frequent pairs of authors are:
| Author 1 | Author 2 | Papers |
|---|---|---|
| Jiwen Lu | Jie Zhou | 57 |
| Luc Van Gool | Radu Timofte | 43 |
| Tao Xiang | Yi-Zhe Song | 38 |
| Fahad Shahbaz Khan | Salman Khan | 33 |
| Ting Yao | Tao Mei | 32 |
| Xiaogang Wang | Hongsheng Li | 28 |
| Shiguang Shan | Xilin Chen | 27 |
| Richa Singh | Mayank Vatsa | 26 |
| Dong Chen | Fang Wen | 24 |
| Yi-Zhe Song | Ayan Kumar Bhunia | 24 |
Although it is quite rare for a paper to have a single author, 185 papers fall into this category. A few worthy mentions are research that introduced novel loss functions (Jonathan T. Barron, Takumi Kobayashi) and improved transformer architectures and post-training techniques (Takumi Kobayashi, Jing Ma). In this table we can see the authors with the most papers where they are the only author:
| Author | Papers |
|---|---|
| Takumi Kobayashi | 4 |
| Anant Khandelwal, Takuhiro Kaneko | 3 |
| Andrey V. Savchenko, Chong Yu, Dimitrios Kollias, Edgar A. Bernal, Jamie Hayes, Magnus Oskarsson, Ming Li, Oleksii Sidorov, Ren Yang, Rowel Atienza, Sanghwa Hong, Satoshi Ikehata, Shunta Maeda, Stamatios Lefkimmiatis, Ying Zhao | 2 |
Identifying Topics
For this section, we used Top2Vec, an automatic topic modeling algorithm, to identify groups of papers that are similar to each other based on their titles and abstracts. The solution found 172 topics, which is a bit too many for us to analyze individually. Instead, we will focus on the hottest and coldest topics, which are those with the most and least papers in the last year, respectively.
One problem with the algorithm is that it identifies topics based on the words used in the papers, but it doesn’t provide a clear explanation of what those topics are about. This is a common problem with topic modeling algorithms, as they often produce results that are difficult to interpret. However, we can use LLMs to help us understand the meaning of these topics. We will use the most representative words of each topic (the words that appear most often in the papers of that topic) to generate a title and a paragraph summarizing it.
🔥 10 topics
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The topics below are listed in the order in which they had the most published articles last year.
Topic 1 - Instruction-Tuned Multimodal LLMs for Vision-Language Understanding (157 documents)
Top authors:
- Yu Qiao (7 papers)
- Ying Shan (5 papers)
- Yixiao Ge (5 papers)
- LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning (2024)
- HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models (2024)
- Rugby Scene Classification Enhanced by Vision Language Model (2024)
Topic 2 - Controllable Text-Guided Image Editing with Diffusion and GAN Inversion (426 documents)
Top authors:
- Chen Change Loy (9 papers)
- Xintao Wang (8 papers)
- Ying Shan (8 papers)
- iEdit: Localised Text-guided Image Editing with Weak Supervision (2024)
- MoMask: Generative Masked Modeling of 3D Human Motions (2024)
- Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing (2024)
Topic 3 - AI-Driven Medical Imaging and Diagnosis in Clinical Practice (345 documents)
Top authors:
- Le Lu (9 papers)
- Faisal Mahmood (5 papers)
- Ke Yan (5 papers)
- SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection (2023)
- A Logarithmic X-Ray Imaging Model for Baggage Inspection: Simulation and Object Detection (2017)
- DiRA: Discriminative, Restorative, and Adversarial Learning for Self-Supervised Medical Image Analysis (2022)
Topic 4 - Challenges and Advances in 3D-Aware Text-to-Image and Text-to-Video Generation (68 documents)
Top authors:
- Hsin-Ying Lee (4 papers)
- Sergey Tulyakov (4 papers)
- Ying Shan (4 papers)
- Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences (2024)
- DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data (2024)
- Diffusion Time-step Curriculum for One Image to 3D Generation (2024)
Topic 5 - Remote Sensing and Aerial Imagery for Environmental and Agricultural Monitoring (306 documents)
Top authors:
- Sara Beery (5 papers)
- David Lobell (4 papers)
- Edward J. Delp (4 papers)
- Unsupervised Learning of Depth and Ego-Motion From Monocular Video Using 3D Geometric Constraints (2018)
- Building Detection From Satellite Imagery Using Ensemble of Size-Specific Detectors (2018)
- On-Orbit Inspection of an Unknown, Tumbling Target Using NASA's Astrobee Robotic Free-Flyers (2021)
Topic 6 - Competitions and Challenges in Computer Vision: The Role of NTIRE and Beyond (90 documents)
Top authors:
- Radu Timofte (44 papers)
- Marcos V. Conde (8 papers)
- Radu Timofte (7 papers)
- NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results (2022)
- Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification (2017)
- NTIRE 2024 Image Shadow Removal Challenge Report (2024)
Topic 7 - Enhancing Diffusion Models: Faster Inference and Higher Image Quality (64 documents)
Top authors:
- Deli Zhao (3 papers)
- Yujun Shen (3 papers)
- Chengyue Gong (2 papers)
- FlowGrad: Controlling the Output of Generative ODEs With Gradients (2023)
- Plug-and-Pipeline: Efficient Regularization for Single-Step Adversarial Training (2020)
- FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits (2023)
Topic 8 - Intelligent Traffic Monitoring and Driver Behavior Analysis for Road Safety (58 documents)
Top authors:
- Armstrong Aboah (3 papers)
- Fei Su (3 papers)
- Zhe Cui (3 papers)
- An Effective Method for Detecting Violation of Helmet Rule for Motorcyclists (2024)
- Stargazer: A Transformer-Based Driver Action Detection System for Intelligent Transportation (2022)
- Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks (2017)
Topic 9 - Soccer and Sports Video Analytics: Player Tracking and Game Understanding (103 documents)
Top authors:
- Anthony Cioppa (11 papers)
- Marc Van Droogenbroeck (10 papers)
- Bernard Ghanem (8 papers)
- Spatio-Temporal Action Detection and Localization Using a Hierarchical LSTM (2020)
- SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos (2018)
- Deep 360 Pilot: Learning a Deep Agent for Piloting Through 360deg Sports Videos (2017)
Topic 10 - Event-Based Vision: High-Speed, Low-Latency Sensing with Neuromorphic Cameras (129 documents)
Top authors:
- Davide Scaramuzza (11 papers)
- Mathias Gehrig (6 papers)
- Boxin Shi (5 papers)
- A Voxel Graph CNN for Object Classification With Event Cameras (2022)
- Generalized Event Cameras (2024)
- EventPS: Real-Time Photometric Stereo Using an Event Camera (2024)
🧊 10 topics
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The topics below are listed in the order in which they had the largest decrease in papers over the last year.
Topic 1 - Self-Supervised Pretraining: Masked Models and Their Impact on Downstream Vision Tasks (115 documents)
Top authors:
- Yu Qiao (9 papers)
- Ishan Misra (4 papers)
- Ross Girshick (4 papers)
- Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers (2024)
- Learning Features by Watching Objects Move (2017)
- Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy (2024)
Topic 2 - Vision-Language Models: Aligning Text and Image for Cross-Modal Understanding (432 documents)
Top authors:
- Lijuan Wang (9 papers)
- Mike Zheng Shou (7 papers)
- Ying Shan (7 papers)
- Image Search With Text Feedback by Visiolinguistic Attention Learning (2020)
- ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding (2023)
- MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training (2024)
Topic 3 - Semi-Supervised Learning: Leveraging Unlabeled Data for Improved Model Performance (179 documents)
Top authors:
- Jingdong Wang (4 papers)
- Lei Qi (4 papers)
- Yinghuan Shi (4 papers)
- Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection (2024)
- Semi-Supervised Learning With Scarce Annotations (2020)
- Phase Consistent Ecological Domain Adaptation (2020)
Topic 4 - Domain Adaptation: Bridging the Gap Between Source and Target Domains (323 documents)
Top authors:
- Luc Van Gool (8 papers)
- Dengxin Dai (7 papers)
- Wen Li (7 papers)
- Contrastive Domain Adaptation (2021)
- DAMSL: Domain Agnostic Meta Score-Based Learning (2021)
- Spatio-temporal Contrastive Domain Adaptation for Action Recognition (2021)
Topic 5 - 3D Object Detection: Advancements in Lidar and Monocular Approaches for Autonomous Vehicles (217 documents)
Top authors:
- Jie Zhou (7 papers)
- Jiwen Lu (6 papers)
- Yuexin Ma (6 papers)
- GAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection (2024)
- SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction (2024)
- IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection (2024)
Topic 6 - Weakly Supervised Object Segmentation: Balancing Annotations and Performance (244 documents)
Top authors:
- Junwei Han (5 papers)
- Yunchao Wei (5 papers)
- Bingfeng Zhang (4 papers)
- CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement (2020)
- SimpSON: Simplifying Photo Cleanup With Single-Click Distracting Object Segmentation Network (2023)
- Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation (2023)
Topic 7 - Image Relighting: Enhancing Lighting and Material Effects in Digital Rendering (167 documents)
Top authors:
- Boxin Shi (11 papers)
- Kalyan Sunkavalli (7 papers)
- Noah Snavely (6 papers)
- Uncalibrated Photometric Stereo Under Natural Illumination (2018)
- Dynamic Fluid Surface Reconstruction Using Deep Neural Network (2020)
- Monocular Reconstruction of Neural Face Reflectance Fields (2021)
Topic 8 - Large Kernel Convolutions and Self-Attention Mechanisms in Vision Transformers (209 documents)
Top authors:
- Xiangyu Zhang (6 papers)
- Chang Xu (5 papers)
- Yu Qiao (5 papers)
- PatchFormer: An Efficient Point Transformer With Patch Attention (2022)
- UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio Video Point Cloud Time-Series and Image Recognition (2024)
- Gaussian Context Transformer (2021)
Topic 9 - 3D-Aware Image Synthesis with GANs for High-Fidelity and Controllable Rendering (71 documents)
Top authors:
- Gordon Wetzstein (4 papers)
- Jiajun Wu (4 papers)
- Sida Peng (4 papers)
- DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-Aware Scene Synthesis (2023)
- NeuralField-LDM: Scene Generation With Hierarchical Latent Diffusion Models (2023)
- Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field (2023)
Topic 10 - Efficient Solving of Non-Convex Problems with Outlier Rejection and Relaxation Techniques (356 documents)
Top authors:
- Daniel Barath (13 papers)
- Daniel Cremers (13 papers)
- Viktor Larsson (10 papers)
- Sliced Optimal Partial Transport (2023)
- 3D Registration With Maximal Cliques (2023)
- A Rotation-Translation-Decoupled Solution for Robust and Efficient Visual-Inertial Initialization (2023)
Conclusion
In this analysis, we have explored the trends and shifts in research topics within the CVPR community over the past years. The data reveals a dynamic landscape, with certain areas experiencing significant growth while others have seen a decline in interest. This reflects the evolving nature of artificial intelligence research and the continuous pursuit of innovation and improvement in various domains.
The hottest topics — ranging from instruction-tuned multimodal LLMs for vision-language understanding to the rapid advancements in text-guided editing, 3D-aware synthesis, and event-based vision — highlight a strong drive toward bridging modalities, creating more controllable generative models, and addressing the growing needs of real-world applications. Researchers are increasingly focusing on improving the integration of language and vision to enable more effective reasoning, better handling of ambiguities (such as hallucinations), and enhanced performance in both creative and safety-critical environments.
In contrast, the coldest topics — such as self-supervised pretraining, traditional vision-language alignment, semi-supervised learning, domain adaptation, and even classical 3D object detection — indicate areas where mature techniques have plateaued. While these methods laid the foundations for current advances, their rate of improvement appears to have slowed in favor of newer approaches. Techniques once at the cutting edge are now being revisited with an eye toward integrating them into more comprehensive systems, but their standalone appeal is declining as the community shifts toward end-to-end, multimodal, and task-specific solutions.
Taken together, the trends suggest that the field is steering toward more holistic, integrated models that not only push the boundaries of what automated systems can generate or analyze but also provide greater reliability and control in real-world applications. As the industry continues to explore the fusion of text, image, and even sensor data, the next wave of innovation will likely be driven by systems that learn from multiple modalities concurrently — while leveraging long-standing, robust principles as a stepping stone.
This evolution underscores the vibrant nature of artificial intelligence research, where established methods provide a stable base while emerging techniques hold the promise of reshaping the future of artificial intelligence.
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