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Works70 from public data
- Group-aware Label Transfer for Domain Adaptive Person Re-identification217
A group-aware Label Transfer (GLT) algorithm is proposed, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning and can better correct the noisy pseudo label in an online fashion and narrow down the search space of the target identity.
- Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization200
This paper introduces Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, and introduces this framework as GI-ReID, which enables an efficient and latency-free person identity matching for surveillance.
- Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification194
This paper proposes to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels, by suppressing the contribution of noisy samples, and introduces uncertainty-guided optimization, which brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
- Cones: Concept Neurons in Diffusion Models for Customized Generation176
This paper finds a small cluster of neurons in a diffusion model corresponding to a particular subject, identified by statistics of network gradients to a stimulation connected with the given subject, which are called concept neurons.
- Advancing Open-source World Models142
LingBot-World is presented, an open-sourced world simulator stemming from video generation that maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond.
- CoDeF: Content Deformation Fields for Temporally Consistent Video Processing141
CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training, and achieves superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog.
- Cones 2: Customizable Image Synthesis with Multiple Subjects114
This work finds that the text embedding regarding the subject token already serves as a simple yet effective representation that supports arbitrary combinations without any model tuning and proposes to employ layout, a very abstract and easy-to-obtain prior, as the spatial guidance for subject arrangement.
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- Hierarchical Gumbel Attention Network for Text-based Person Search106
A novel hierarchical Gumbel attention network for text-based person search via Gumbels top-k re-parameterization algorithm that adaptively selects the strong semantically relevant image regions and words/phrases from images and texts for precise alignment and similarity calculation.
- Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification87
A network named Pose-Guided Feature Learning with Knowledge Distillation (PGFL-KD), where the pose information is exploited to regularize the learning of semantics aligned features but is discarded in testing to achieve high accuracy while preserving low inference complexity.
- Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos80
A novel Spatial-Temporal Correlation and Topology Learning framework is proposed to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation by utilizing a CNN backbone and a key-points estimator to extract semantic local features from human body at multiple granularities as graph nodes.
- Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning40
A detailed caption benchmark, termed as CompreCap, to evaluate the visual context from a directed scene graph view, to assess the generated detailed captions from LVLMs on multiple levels, including the object-level coverage, the accuracy of attribute descriptions, the score of key relationships, etc.
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- Framer: Interactive Frame Interpolation33
This work proposes Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity, and supports customizing the transition process by tailoring the trajectory of some selected keypoints.
- Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection32
A novel uncertainty-aware optimal transport scheme that estimates the fluctuating cost of uncertainty to promote the assignment of semantic-agnostic representation, and an inter-cluster extension strategy that enhances the discrimination of semantic property among different clusters by widening the corresponding margin distance is presented.
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- Mimir: Improving Video Diffusion Models for Precise Text Understanding20
Mimir is an end-to-end training framework featuring a carefully tailored token fuser to harmonize the outputs from text encoders and LLMs, which allows the T2V model to fully leverage learned video priors while capitalizing on the text-related capability of LLMs.
- Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis19
Aurora, a GAN-based text-to-image generator that employs a collection of experts to learn feature processing, together with a sparse router to adaptively select the most suitable expert for each feature point, closes the performance gap between GANs and industry-level diffusion models, maintaining a fast inference speed.
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- MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation18
A new motion estimator, capable of measuring the decoupled motion intensities of objects and cameras in video, developed with a new I2V model, named MotionStone, developed with the decoupled motion estimator.
- UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training18
This work categorizes data knowledge into five unit types, namely, in-image, in-text, cross-image, cross-text, and image-text, and sets up an efficient pipeline to help construct the multimodal knowledge graph from any data collection.
- Contextual AD Narration with Interleaved Multimodal Sequence17
This work proposes a simple and lightweight module that maps video features into the textual feature space and proposes a character-refinement module to provide more precise information by identifying the main characters who play more significant roles in the video context.
- LA-Net16
A novel Layout-Aware Convolutional Neural Network (LA-Net) is proposed for accurate monocular depth estimation by simultaneously perceiving scene layout and local depth details and formulate an order-sensitive softmax loss to well constrain the ill-posed depth inferring problem.
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- Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models14
This work designs a new type of tuning method, termed as regularized mask tuning, which masks the network parameters through a learnable selection, and is synergistic with most existing parameter-efficient tuning methods and can boost the performance on top of them.
- Stacked Convolutional Deep Encoding Network For Video-Text Retrieval12
A stacked convolutional deep encoding network for video-text retrieval task, which considers to simultaneously encode long-range and short-range dependency in the videos and texts to obtain more robust textual representations.
- FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization11
This work proposes a novel frequency-aware MLP architecture, in which the domain-specific features are filtered out in the transformed frequency domain, augmenting the invariant descriptor for label prediction, and is the first to propose a MLP-like backbone for domain generalization.
- Principled Knowledge Extrapolation with GANs10
It is shown that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem, and a novel principal knowledge descent method can efficiently estimate the extrapolated distribution through the adversarial game.
- Learning Visual Generative Priors without Text8
This work develops a pure vision-based training framework, Lumos, and confirms the feasibility and the scalability of learning I2I models, and finds that, as an upstream task of T2I, the I2I model serves as a more foundational visual prior and achieves on-par or better performance than existing T2I models using only 1/10 text-image pairs for fine-tuning.
- TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification8
This work proposes an embarrassingly simple approach to better align image and text features with no need of additional data formats other than image-text pairs and indicates that attribute supervision makes vision-language models accurately localize attribute-specified objects.
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- Exert Diversity and Mitigate Bias: Domain Generalizable Person Re-identification with a Comprehensive Benchmark5
A novel DG-ReID framework based on diverse feature space learning with domain factorization is proposed to effectively learn rich domain-adaptive discriminative features through the two designed blocks with fairly limited additional cost in both memory and computation.
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- Learning Naturally Aggregated Appearance for Efficient 3D Editing4
This work proposes to learn the color field as an explicit 2D appearance aggregation, also called canonical image, with which users can easily customize their 3D editing via 2D image processing, and complement the canonical image with a projection field that maps 3D points onto 2D pixels for texture query.
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- GenDeF: Learning Generative Deformation Field for Video Generation3
This work proposes to render a video by warping one static image with a generative deformation field (GenDeF), alleviating the difficulty in producing a video and thereby resulting in better visual quality.
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- Reflective Cross-Granularity Grounding with Preference Optimization for Long Video Understanding–
ReCrossVLLM is proposed, a reflective cross-granularity grounding framework for video LLM with preference optimization to collaboratively achieve long video understanding, which not only retains the capabilities of high-level video semantics understanding, but also strengthens the fine-grained understanding abilities.
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- Neural Dependencies Emerging from Learning Massive Categories–
It is demonstrated that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper, and the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer is shown.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. 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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