CusConcept: Customized Visual Concept Decomposition with Diffusion Models

Enabling generative models to decompose visual concepts from a single image is a complex and challenging problem. In this paper, we study a new and challenging task, customized concept decomposition, wherein the objective is to leverage diffusion models to decompose a single image and generate visual concepts from various perspectives. To address this challenge, we propose a two-stage framework, CusConcept (short for Customized Visual Concept Decomposition), to extract customized visual concept embedding vectors that can be embedded into prompts for text-to-image generation. In the first stage, CusConcept employs a vocabulary-guided concept decomposition mechanism to build vocabularies along human-specified conceptual axes. The decomposed concepts are obtained by retrieving corresponding vocabularies and learning anchor weights. In the second stage, joint concept refinement is performed to enhance the fidelity and quality of generated images. We further curate an evaluation benchmark for assessing the performance of the open-world concept decomposition task. Our approach can effectively generate high-quality images of the decomposed concepts and produce related lexical predictions as secondary outcomes. Extensive qualitative and quantitative experiments demonstrate the effectiveness of CusConcept.

Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and Benchmarks

Detecting test-time distribution shift has emerged as a key capability for safely deployed machine learning models, with the question being tackled under various guises in recent years. In this paper, we aim to provide a consolidated view of the two largest sub-fields within the community: out-of-distribution (OOD) detection and open-set recognition (OSR). In particular, we aim to provide rigorous empirical analysis of different methods across settings and provide actionable takeaways for practitioners and researchers. Concretely, we make the following contributions: (i) We perform rigorous cross-evaluation between state-of-the-art methods in the OOD detection and OSR settings and identify a strong correlation between the performances of methods for them; (ii) We propose a new, large-scale benchmark setting which we suggest better disentangles the problem tackled by OOD detection and OSR, re-evaluating state-of-the-art OOD detection and OSR methods in this setting; (iii) We surprisingly find that the best performing method on standard benchmarks (Outlier Exposure) struggles when tested at scale, while scoring rules which are sensitive to the deep feature magnitude consistently show promise; and (iv) We conduct empirical analysis to explain these phenomena and highlight directions for future research. Code: https://github.com/Visual-AI/Dissect-OOD-OSR

GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models

Large multimodal models (LMMs) have exhibited proficiencies across many visual tasks. Although numerous well-known benchmarks exist to evaluate model performance, they increasingly have insufficient headroom. As such, there is a pressing need for a new generation of benchmarks challenging enough for the next generation of LMMs. One area that LMMs show potential is graph analysis, specifically, the tasks an analyst might typically perform when interpreting figures such as estimating the mean, intercepts or correlations of functions and data series. In this work, we introduce GRAB, a graph analysis benchmark, fit for current and future frontier LMMs. Our benchmark is entirely synthetic, ensuring high-quality, noise-free questions. GRAB is comprised of 2170 questions, covering four tasks and 23 graph properties. We evaluate 20 LMMs on GRAB, finding it to be a challenging benchmark, with the highest performing model attaining a score of just 21.7%. Finally, we conduct various ablations to investigate where the models succeed and struggle. We release GRAB to encourage progress in this important, growing domain.

Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning

Token compression expedites the training and inference of Vision Transformers (ViTs) by reducing the number of the redundant tokens, e.g., pruning inattentive tokens or merging similar tokens. However, when applied to downstream tasks, these approaches suffer from significant performance drop when the compression degrees are mismatched between training and inference stages, which limits the application of token compression on off-the-shelf trained models. In this paper, we propose a model arithmetic framework to decouple the compression degrees between the two stages. In advance, we additionally perform a fast parameter-efficient self-distillation stage on the pre-trained models to obtain a small plugin, called Token Compensator (ToCom), which describes the gap between models across different compression degrees. During inference, ToCom can be directly inserted into any downstream off-the-shelf models with any mismatched training and inference compression degrees to acquire universal performance improvements without further training. Experiments on over 20 downstream tasks demonstrate the effectiveness of our framework. On CIFAR100, fine-grained visual classification, and VTAB-1k, ToCom can yield up to a maximum improvement of 2.3%, 1.5%, and 2.0% in the average performance of DeiT-B, respectively. Code: https://github.com/JieShibo/ToCom

HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts

Generalized Category Discovery (GCD) is a challenging task in which, given a partially labelled dataset, models must categorize all unlabelled instances, regardless of whether they come from labelled categories or from new ones. In this paper, we challenge a remaining assumption in this task: that all images share the same domain. Specifically, we introduce a new task and method to handle GCD when the unlabelled data also contains images from different domains to the labelled set. Our proposed `HiLo' networks extract High-level semantic and Low-level domain features, before minimizing the mutual information between the representations. Our intuition is that the clusterings based on domain information and semantic information should be independent. We further extend our method with a specialized domain augmentation tailored for the GCD task, as well as a curriculum learning approach. Finally, we construct a benchmark from corrupted fine-grained datasets as well as a large-scale evaluation on DomainNet with real-world domain shifts, reimplementing a number of GCD baselines in this setting. We demonstrate that HiLo outperforms SoTA category discovery models by a large margin on all evaluations.

LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework

Histological artifacts pose challenges for both pathologists and Computer-Aided Diagnosis (CAD) systems, leading to errors in analysis. Current approaches for histological artifact restoration, based on Generative Adversarial Networks (GANs) and pixel-level Diffusion Models, suffer from performance limitations and computational inefficiencies. In this paper, we propose a novel framework, LatentArtiFusion, which leverages the latent diffusion model (LDM) to reconstruct histological artifacts with high performance and computational efficiency. Unlike traditional pixel-level diffusion frameworks, LatentArtiFusion executes the restoration process in a lower-dimensional latent space, significantly improving computational efficiency. Moreover, we introduce a novel regional artifact reconstruction algorithm in latent space to prevent mistransfer in non-artifact regions, distinguishing our approach from GAN-based methods. Through extensive experiments on real-world histology datasets, LatentArtiFusion demonstrates remarkable speed, outperforming state-of-the-art pixel-level diffusion frameworks by more than 30X. It also consistently surpasses GAN-based methods by at least 5% across multiple evaluation metrics. Furthermore, we evaluate the effectiveness of our proposed framework in downstream tissue classification tasks, showcasing its practical utility. Code is available at https://github.com/bugs-creator/LatentArtiFusion.

PromptCCD: Learning Gaussian Mixture Prompt Pool for Continual Category Discovery

We tackle the problem of Continual Category Discovery (CCD), which aims to automatically discover novel categories in a continuous stream of unlabeled data while mitigating the challenge of catastrophic forgetting -- an open problem that persists even in conventional, fully supervised continual learning. To address this challenge, we propose PromptCCD, a simple yet effective framework that utilizes a Gaussian Mixture Model (GMM) as a prompting method for CCD. At the core of PromptCCD lies the Gaussian Mixture Prompting (GMP) module, which acts as a dynamic pool that updates over time to facilitate representation learning and prevent forgetting during category discovery. Moreover, GMP enables on-the-fly estimation of category numbers, allowing PromptCCD to discover categories in unlabeled data without prior knowledge of the category numbers. We extend the standard evaluation metric for Generalized Category Discovery (GCD) to CCD and benchmark state-of-the-art methods on diverse public datasets. PromptCCD significantly outperforms existing methods, demonstrating its effectiveness. Project page: https://visual-ai.github.io/promptccd .

RegionDrag: Fast Region-Based Image Editing with Diffusion Models

Point-drag-based image editing methods, like DragDiffusion, have attracted significant attention. However, point-drag-based approaches suffer from computational overhead and misinterpretation of user intentions due to the sparsity of point-based editing instructions. In this paper, we propose a region-based copy-and-paste dragging method, RegionDrag, to overcome these limitations. RegionDrag allows users to express their editing instructions in the form of handle and target regions, enabling more precise control and alleviating ambiguity. In addition, region-based operations complete editing in one iteration and are much faster than point-drag-based methods. We also incorporate the attention-swapping technique for enhanced stability during editing. To validate our approach, we extend existing point-drag-based datasets with region-based dragging instructions. Experimental results demonstrate that RegionDrag outperforms existing point-drag-based approaches in terms of speed, accuracy, and alignment with user intentions. Remarkably, RegionDrag completes the edit on an image with a resolution of 512x512 in less than 2 seconds, which is more than 100x faster than DragDiffusion, while achieving better performance. Project page: https://visual-ai.github.io/regiondrag.

ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction

While personalized text-to-image generation has enabled the learning of a single concept from multiple images, a more practical yet challenging scenario involves learning multiple concepts within a single image. However, existing works tackling this scenario heavily rely on extensive human annotations. In this paper, we introduce a novel task named Unsupervised Concept Extraction (UCE) that considers an unsupervised setting without any human knowledge of the concepts. Given an image that contains multiple concepts, the task aims to extract and recreate individual concepts solely relying on the existing knowledge from pretrained diffusion models. To achieve this, we present ConceptExpress that tackles UCE by unleashing the inherent capabilities of pretrained diffusion models in two aspects. Specifically, a concept localization approach automatically locates and disentangles salient concepts by leveraging spatial correspondence from diffusion self-attention; and based on the lookup association between a concept and a conceptual token, a concept-wise optimization process learns discriminative tokens that represent each individual concept. Finally, we establish an evaluation protocol tailored for the UCE task. Extensive experiments demonstrate that ConceptExpress is a promising solution to the UCE task. Our code and data are available at: https://github.com/haoosz/ConceptExpress

A Survey on 3D Human Avatar Modeling -- From Reconstruction to Generation

3D modeling has long been an important area in computer vision and computer graphics. Recently, thanks to the breakthroughs in neural representations and generative models, we witnessed a rapid development of 3D modeling. 3D human modeling, lying at the core of many real-world applications, such as gaming and animation, has attracted significant attention. Over the past few years, a large body of work on creating 3D human avatars has been introduced, forming a new and abundant knowledge base for 3D human modeling. The scale of the literature makes it difficult for individuals to keep track of all the works. This survey aims to provide a comprehensive overview of these emerging techniques for 3D human avatar modeling, from both reconstruction and generation perspectives. Firstly, we review representative methods for 3D human reconstruction, including methods based on pixel-aligned implicit function, neural radiance field, and 3D Gaussian Splatting, etc. We then summarize representative methods for 3D human generation, especially those using large language models like CLIP, diffusion models, and various 3D representations, which demonstrate state-of-the-art performance. Finally, we discuss our reflection on existing methods and open challenges for 3D human avatar modeling, shedding light on future research.