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Works42 from public data
- ByteTrack: Multi-object Tracking by Associating Every Detection Box2,841
A simple, effective and generic association method, tracking by associating almost every detection box instead of only the high score ones, named ByteTrack, which achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
- Sparse R-CNN: End-to-End Object Detection with Learnable Proposals1,564
Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well-established detector baselines on the challenging COCO dataset, e.g., achieving 45.0 AP in standard 3× training schedule and running at 22 fps using ResNet-50 FPN model.
- SAM 3: Segment Anything with Concepts1,205
The Segment Anything Model (SAM) 3 is presented, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which are either short noun phrases or image exemplars, or a combination of both.
- Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation833
LlamaGen is introduced, a new family of image generation models that apply original ``next-token prediction'' paradigm of large language models to visual generation domain, an affirmative answer to whether vanilla autoregressive models can achieve state-of-the-art image generation performance if scaling properly.
- DiffusionDet: Diffusion Model for Object Detection811
The extensive experiments show that DiffusionDet achieves favorable performance compared to previous well-established detectors, and possesses an appealing property of flexibility, which enables the dynamic number of boxes and iterative evaluation.
- PolarMask: Single Shot Instance Segmentation With Polar Representation641
This paper introduces an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used by easily embedding it into most off-the-shelf detection methods.
- DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion467
A large-scale dataset for multi-human tracking, where humans have sim-ilar appearance, diverse motion and extreme articulation, and is named “DanceTrack”, which is expected to provide a better platform to develop more MOT algorithms that rely less on visual discrimination and depend more on motion analysis.
- DetCo: Unsupervised Contrastive Learning for Object Detection379
Extensive experiments demonstrate that DetCo not only outperforms recent methods on a series of 2D and 3D instance-level detection tasks, but also competitive on image classification.
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- Language as Queries for Referring Video Object Segmentation282
This work proposes a simple and unified framework built upon Transformer, termed ReferFormer, which views the language as queries and directly attends to the most relevant regions in the video frames, and significantly outperforms the previous methods by a large margin.
- Semantic-SAM: Segment and Recognize Anything at Any Granularity272
Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity, is introduced, representing the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets.
- A deep-learning-based approach for fast and robust steel surface defects classification267
A compact yet effective convolutional neural network model, which emphasizes the training of low-level features and incorporates multiple receptive fields, to achieve fast and accurate steel surface defect classification and adopts the pre-trained SqueezeNet as the backbone architecture.
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- Learning Object-Language Alignments for Open-Vocabulary Object Detection144
This paper proposes a novel open-vocabulary object detection framework directly learning from image-text pair data as a set matching problem between a set of image region features and aSet of word embeddings, which enables us to train an open- Vocabulary object detector on image- Text pairs in a much simple and effective way.
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- Segmenting Transparent Objects in the Wild with Transformer109
A novel Transformer-based segmentation pipeline termed Trans2Seg is proposed, which significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.
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- Going Denser with Open-Vocabulary Part Segmentation99
A detector with the ability to predict both open-vocabulary objects and their part segmentation and a detector that generalizes to a wide range of part segmentation datasets while achieving better performance than dataset-specific training is proposed.
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- Goku: Flow Based Video Generative Foundation Models82
The foundational elements enabling high-quality visual generation, including the data curation pipeline, model architecture design, flow formulation, and advanced infrastructure for efficient and robust large-scale training are detailed.
- Segment and Recognize Anything at Any Granularity81
This work introduces Semantic-SAM, an augmented image segmentation foundation for segmenting and recognizing anything at desired granularities, and is the first attempt to jointly train a model on SA-1B, instance-level, and part-level segmentation datasets.
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- RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis66
A tree-structured multimodal code generation framework for generalized robotic behavior synthesis, termed RoboCodeX, which decomposes high-level human instructions into multiple object-centric manipulation units consisting of physical preferences such as affordance and safety constraints, and applies code generation to introduce generalization ability across various robotics platforms.
- ControlAR: Controllable Image Generation with Autoregressive Models55
ControlAR is introduced, an efficient and effective framework for integrating spatial controls into autoregressive image generation models and surpasses previous state-of-the-art controllable diffusion models, e.g., ControlNet++.
- ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box51
A complementary motion prediction strategy that incorporates the detected velocities with a Kalman filter to address the problem of abrupt motion and short-term disappearing is proposed.
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- OneNet: Towards End-to-End One-Stage Object Detection.33
This paper discovers that the lack of classification cost between sample and ground-truth in label assignment is the main obstacle for one-stage detectors to remove Non-maximum Suppression (NMS) and reach end-to-end.
- Prompt-A-Video: Prompt your Video Diffusion Model via Preference-Aligned LLM25
This work introduces an LLMbased prompt adaptation framework, termed as Prompt-A-Video, which excels in crafting Video-Centric, LaborFree and Preference-Aligned prompts tailored to specific video diffusion model, and validate the effectiveness of Prompt-A-Video across diverse generation models.
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- Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models21
Token-Shuffle is presented, a novel yet simple method that reduces the number of image tokens in Transformer that enables MLLMs to support extremely high-resolution image synthesis in a unified next-token prediction way while maintaining efficient training and inference.
- Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment16
Experimental results show that CCA can significantly enhance the guidance-free performance of all tested models with just one epoch of fine-tuning on the pretraining dataset, on par with guided sampling methods, and largely removes the need for guided sampling in AR visual generation and cuts the sampling cost by half.
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- IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model11
The research introduces a novel benchmark MM-ID, to examine LVLMs on instance IDs memory and recognition across four dimensions: matching, location, question-answering, and captioning, and highlights the limitations of existing LVLMs in recognizing and associating instance identities with ID reference.
- OmniGen-AR: AutoRegressive Any-to-Image Generation7
This work presents OmniGen-AR, a unified autoregressive framework for Any-to-Image generation that supports a broad spectrum of conditional inputs within a single model, and introduces Disentangled Causal Attention (DCA), which separates the full-sequence causal mask into condition causal attention and content causal attention.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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