Text-to-image person re-identification (ReID) retrieves pedestrian images according to textual descriptions. Manually annotating textual descriptions is time-consuming restricting the scale of existing datasets and therefore the generalization ability of ReID models. As a result we study the transferable text-to-image ReID problem where we train a model on our proposed large-scale database and directly deploy it to various datasets for evaluation. We obtain substantial training data via Multi-modal Large Language Models (MLLMs). Moreover we identify and address two key challenges in utilizing the obtained textual descriptions. First an MLLM tends to generate descriptions with similar structures causing the model to overfit specific sentence patterns. Thus we propose a novel method that uses MLLMs to caption images according to various templates. These templates are obtained using a multi-turn dialogue with a Large Language Model (LLM). Therefore we can build a large-scale dataset with diverse textual descriptions. Second an MLLM may produce incorrect descriptions. Hence we introduce a novel method that automatically identifies words in a description that do not correspond with the image. This method is based on the similarity between one text and all patch token embeddings in the image. Then we mask these words with a larger probability in the subsequent training epoch alleviating the impact of noisy textual descriptions. The experimental results demonstrate that our methods significantly boost the direct transfer text-to-image ReID performance. Benefiting from the pre-trained model weights we also achieve state-of-the-art performance in the traditional evaluation settings.
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Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning
PDF ↗Over the past year a large body of multimodal research has emerged around zero-shot evaluation using GPT descriptors. These studies boost the zero-shot accuracy of pretrained VL models with an ensemble of label-specific text generated by GPT. A recent study WaffleCLIP demonstrated that similar zero-shot accuracy can be achieved with an ensemble of random descriptors. However both zero-shot methods are un-trainable and consequently sub-optimal when some few-shot out-of-distribution (OOD) training data is available. Inspired by these prior works we present two more flexible methods called descriptor and word soups which do not require an LLM at test time and can leverage training data to increase OOD target accuracy. Descriptor soup greedily selects a small set of textual descriptors using generic few-shot training data then calculates robust class embeddings using the selected descriptors. Word soup greedily assembles a chain of words in a similar manner. Compared to existing few-shot soft prompt tuning methods word soup requires fewer parameters by construction and less GPU memory since it does not require backpropagation. Both soups outperform current published few-shot methods even when combined with SoTA zero-shot methods on cross-dataset and domain generalization benchmarks. Compared with SoTA prompt and descriptor ensembling methods such as ProDA and WaffleCLIP word soup achieves higher OOD accuracy with fewer ensemble members. Please checkout our code: https://github.com/Chris210634/word_soups
Finding correspondences between 3D shapes is an important and long-standing problem in computer vision graphics and beyond. A prominent challenge are partial-to-partial shape matching settings which occur when the shapes to match are only observed incompletely (e.g. from 3D scanning). Although partial-to-partial matching is a highly relevant setting in practice it is rarely explored. Our work bridges the gap between existing (rather artificial) 3D full shape matching and partial-to-partial real-world settings by exploiting geometric consistency as a strong constraint. We demonstrate that it is indeed possible to solve this challenging problem in a variety of settings. For the first time we achieve geometric consistency for partial-to-partial matching which is realized by a novel integer non-linear program formalism building on triangle product spaces along with a new pruning algorithm based on linear integer programming. Further we generate a new inter-class dataset for partial-to-partial shape-matching. We show that our method outperforms current SOTA methods on both an established intra-class dataset and our novel inter-class dataset.
Capturing and re-animating the 3D structure of articulated objects present significant barriers. On one hand methods requiring extensively calibrated multi-view setups are prohibitively complex and resource-intensive limiting their practical applicability. On the other hand while single-camera Neural Radiance Fields (NeRFs) offer a more streamlined approach they have excessive training and rendering costs. 3D Gaussian Splatting would be a suitable alternative but for two reasons. Firstly existing methods for 3D dynamic Gaussians require synchronized multi-view cameras and secondly the lack of controllability in dynamic scenarios. We present CoGS a method for Controllable Gaussian Splatting that enables the direct manipulation of scene elements offering real-time control of dynamic scenes without the prerequisite of pre-computing control signals. We evaluated CoGS using both synthetic and real-world datasets that include dynamic objects that differ in degree of difficulty. In our evaluations CoGS consistently outperformed existing dynamic and controllable neural representations in terms of visual fidelity.
Creating high-quality materials in computer graphics is a challenging and time-consuming task which requires great expertise. To simplify this process we introduce MatFuse a unified approach that harnesses the generative power of diffusion models for creation and editing of 3D materials. Our method integrates multiple sources of conditioning including color palettes sketches text and pictures enhancing creative possibilities and granting fine-grained control over material synthesis. Additionally MatFuse enables map-level material editing capabilities through latent manipulation by means of a multi-encoder compression model which learns a disentangled latent representation for each map. We demonstrate the effectiveness of MatFuse under multiple conditioning settings and explore the potential of material editing. Finally we assess the quality of the generated materials both quantitatively in terms of CLIP-IQA and FID scores and qualitatively by conducting a user study. Source code for training MatFuse and supplemental materials are publicly available at https://gvecchio.com/matfuse.
Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks and exhibit remarkable zero-shot generalization capability while they are also vulnerable to imperceptible adversarial examples. Existing works typically employ adversarial training (fine-tuning) as a defense method against adversarial examples. However direct application to the CLIP model may result in overfitting compromising the model's capacity for generalization. In this paper we propose Pre-trained Model Guided Adversarial Fine-Tuning (PMG-AFT) method which leverages supervision from the original pre-trained model by carefully designing an auxiliary branch to enhance the model's zero-shot adversarial robustness. Specifically PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model aiming to preserve the generalization features already captured by the pre-trained model. Extensive Experiments on 15 zero-shot datasets demonstrate that PMG-AFT significantly outperforms the state-of-the-art method improving the top-1 robust accuracy by an average of 4.99%. Furthermore our approach consistently improves clean accuracy by an average of 8.72%.
Multimodal intent recognition (MIR) aims to perceive the human intent polarity via language visual and acoustic modalities. The inherent intent ambiguity makes it challenging to recognize in multimodal scenarios. Existing MIR methods tend to model the individual video independently ignoring global contextual information across videos. This learning manner inevitably introduces perception biases exacerbated by the inconsistencies of the multimodal representation amplifying the intent uncertainty. This challenge motivates us to explore effective global context modeling. Thus we propose a context-augmented global contrast (CAGC) method to capture rich global context features by mining both intra-and cross-video context interactions for MIR. Concretely we design a context-augmented transformer module to extract global context dependencies across videos. To further alleviate error accumulation and interference we develop a cross-video bank that retrieves effective video sources by considering both intentional tendency and video similarity. Furthermore we introduce a global context-guided contrastive learning scheme designed to mitigate inconsistencies arising from global context and individual modalities in different feature spaces. This scheme incorporates global cues as the supervision to capture robust the multimodal intent representation. Experiments demonstrate CAGC obtains superior performance than state-of-the-art MIR methods. We also generalize our approach to a closely related task multimodal sentiment analysis achieving the comparable performance.
We present a novel method for efficiently producing semi-dense matches across images. Previous detector-free matcher LoFTR has shown remarkable matching capability in handling large-viewpoint change and texture-poor scenarios but suffers from low efficiency. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. One key observation is that performing the transformer over the entire feature map is redundant due to shared local information therefore we propose an aggregated attention mechanism with adaptive token selection for efficiency. Furthermore we find spatial variance exists in LoFTR's fine correlation module which is adverse to matching accuracy. A novel two-stage correlation layer is proposed to achieve accurate subpixel correspondences for accuracy improvement. Our efficiency optimized model is ~ 2.5xfaster than LoFTR which can even surpass state-of-the-art efficient sparse matching pipeline SuperPoint + LightGlue. Moreover extensive experiments show that our method can achieve higher accuracy compared with competitive semi-dense matchers with considerable efficiency benefits. This opens up exciting prospects for large-scale or latency-sensitive applications such as image retrieval and 3D reconstruction. Project page: https://zju3dv.github.io/efficientloftr/.
Recent advances in generative AI have unveiled significant potential for the creation of 3D content. However current methods either apply a pre-trained 2D diffusion model with the time-consuming score distillation sampling (SDS) or a direct 3D diffusion model trained on limited 3D data losing generation diversity. In this work we approach the problem by employing a multi-view 2.5D diffusion fine-tuned from a pre-trained 2D diffusion model. The multi-view 2.5D diffusion directly models the structural distribution of 3D data while still maintaining the strong generalization ability of the original 2D diffusion model filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation. During inference multi-view normal maps are generated using the 2.5D diffusion and a novel differentiable rasterization scheme is introduced to fuse the almost consistent multi-view normal maps into a consistent 3D model. We further design a normal-conditioned multi-view image generation module for fast appearance generation given the 3D geometry. Our method is a one-pass diffusion process and does not require any SDS optimization as post-processing. We demonstrate through extensive experiments that our direct 2.5D generation with the specially-designed fusion scheme can achieve diverse mode-seeking-free and high-fidelity 3D content generation in only 10 seconds.
We propose a novel contrastive learning framework to effectively address the challenges of data heterogeneity in federated learning. We first analyze the inconsistency of gradient updates across clients during local training and establish its dependence on the distribution of feature representations leading to the derivation of the supervised contrastive learning (SCL) objective to mitigate local deviations. In addition we show that a naive integration of SCL into federated learning incurs representation collapse resulting in slow convergence and limited performance gains. To address this issue we introduce a relaxed contrastive learning loss that imposes a divergence penalty on excessively similar sample pairs within each class. This strategy prevents collapsed representations and enhances feature transferability facilitating collaborative training and leading to significant performance improvements. Our framework outperforms all existing federated learning approaches by significant margins on the standard benchmarks as demonstrated by extensive experimental results. The source code is available at our project page(https://github.com/skynbe/FedRCL).
Existing text-to-image (T2I) diffusion models usually struggle in interpreting complex prompts especially those with quantity object-attribute binding and multi-subject descriptions. In this work we introduce a semantic panel as the middleware in decoding texts to images supporting the generator to better follow instructions. The panel is obtained through arranging the visual concepts parsed from the input text by the aid of large language models and then injected into the denoising network as a detailed control signal to complement the text condition. To facilitate text-to-panel learning we come up with a carefully designed semantic formatting protocol accompanied by a fully-automatic data preparation pipeline. Thanks to such a design our approach which we call Ranni manages to enhance a pre-trained T2I generator regarding its textual controllability. More importantly the introduction of the generative middleware brings a more convenient form of interaction (i.e. directly adjusting the elements in the panel or using language instructions) and further allows users to finely customize their generation based on which we develop a practical system and showcase its potential in continuous generation and chatting-based editing.
Low-shot counters estimate the number of objects corresponding to a selected category based on only few or no exemplars annotated in the image. The current state-of-the-art estimates the total counts as the sum over the object location density map but do not provide object locations and sizes which are crucial for many applications. This is addressed by detection-based counters which however fall behind in the total count accuracy. Furthermore both approaches tend to overestimate the counts in the presence of other object classes due to many false positives. We propose DAVE a low-shot counter based on a detect-and-verify paradigm that avoids the aforementioned issues by first generating a high-recall detection set and then verifying the detections to identify and remove the outliers.This jointly increases the recall and precision leading to accurate counts. DAVE outperforms the top density-based counters by ?20% in the total count MAE it outperforms the most recent detection-based counter by ?20% in detection quality and sets a new state-of-the-art in zero-shot as well as text-prompt-based counting.
To synthesize high-fidelity samples diffusion models typically require auxiliary data to guide the generation process. However it is impractical to procure the painstaking patch-level annotation effort required in specialized domains like histopathology and satellite imagery; it is often performed by domain experts and involves hundreds of millions of patches. Modern-day self-supervised learning (SSL) representations encode rich semantic and visual information. In this paper we posit that such representations are expressive enough to act as proxies to fine-grained human labels. We introduce a novel approach that trains diffusion models conditioned on embeddings from SSL. Our diffusion models successfully project these features back to high-quality histopathology and remote sensing images. In addition we construct larger images by assembling spatially consistent patches inferred from SSL embeddings preserving long-range dependencies. Augmenting real data by generating variations of real images improves downstream classifier accuracy for patch-level and larger image-scale classification tasks. Our models are effective even on datasets not encountered during training demonstrating their robustness and generalizability. Generating images from learned embeddings is agnostic to the source of the embeddings. The SSL embeddings used to generate a large image can either be extracted from a reference image or sampled from an auxiliary model conditioned on any related modality (e.g. class labels text genomic data). As proof of concept we introduce the text-to-large image synthesis paradigm where we successfully synthesize large pathology and satellite images out of text descriptions.
Adversarial training is extensively utilized to improve the adversarial robustness of deep neural networks. Yet mitigating the degradation of standard generalization performance in adversarial-trained models remains an open problem. This paper attempts to resolve this issue through the lens of model complexity. First We leverage the Fisher-Rao norm a geometrically invariant metric for model complexity to establish the non-trivial bounds of the Cross-Entropy Loss-based Rademacher complexity for a ReLU-activated Multi-Layer Perceptron. Building upon this observation we propose a novel regularization framework called Logit-Oriented Adversarial Training (LOAT) which can mitigate the trade-off between robustness and accuracy while imposing only a negligible increase in computational overhead. Our extensive experiments demonstrate that the proposed regularization strategy can boost the performance of the prevalent adversarial training algorithms including PGD-AT TRADES TRADES (LSE) MART and DM-AT across various network architectures. Our code will be available at https://github.com/TrustAI/LOAT.
Depth completion is a vital task for autonomous driving as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation which are still insufficient to capture the fine-grained 3D geometry of the scene. To address this challenge we introduce Tri-Perspective View Decomposition (TPVD) a novel framework that can explicitly model 3D geometry. In particular (1) TPVD ingeniously decomposes the original point cloud into three 2D views one of which corresponds to the sparse depth input. (2) We design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D aggregation where a Distance-Aware Spherical Convolution (DASC) is applied. (3) By adaptively choosing TPV affinitive neighbors the newly proposed Geometric Spatial Propagation Network (GSPN) further improves the geometric consistency. As a result our TPVD outperforms existing methods on KITTI NYUv2 and SUN RGBD. Furthermore we build a novel depth completion dataset named TOFDC which is acquired by the time-of-flight (TOF) sensor and the color camera on smartphones.
This paper presents Neural Visibility Field (NVF) a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that regions not visible in the training views lead to inherently unreliable color predictions by NeRF at this region resulting in increased uncertainty in the synthesized views. To address this we propose to use Bayesian Networks to composite position-based field uncertainty into ray-based uncertainty in camera observations. Consequently NVF naturally assigns higher uncertainty to unobserved regions aiding robots to select the most informative next viewpoints. Extensive evaluations show that NVF excels not only in uncertainty quantification but also in scene reconstruction for active mapping outperforming existing methods. More details can be found at https://sites.google.com/view/nvf-cvpr24/.
We present En3D an enhanced generative scheme for sculpting high-quality 3D human avatars. Unlike previous works that rely on scarce 3D datasets or limited 2D collections with imbalanced viewing angles and imprecise pose priors our approach aims to develop a zero-shot 3D generative scheme capable of producing visually realistic geometrically accurate and content-wise diverse 3D humans without relying on pre-existing 3D or 2D assets. To address this challenge we introduce a meticulously crafted workflow that implements accurate physical modeling to learn the enhanced 3D generative model from synthetic 2D data. During inference we integrate optimization modules to bridge the gap between realistic appearances and coarse 3D shapes. Specifically En3D comprises three modules: a 3D generator that accurately models generalizable 3D humans with realistic appearance from synthesized balanced diverse and structured human images; a geometry sculptor that enhances shape quality using multi-view normal constraints for intricate human structure; and a texturing module that disentangles explicit texture maps with fidelity and editability leveraging semantical UV partitioning and a differentiable rasterizer. Experimental results show that our approach significantly outperforms prior works in terms of image quality geometry accuracy and content diversity. We also showcase the applicability of our generated avatars for animation and editing as well as the scalability of our approach for content-style free adaptation.
Predicting human pose sequences via existing pose estimators often encounters various estimation errors. Motion refinement methods aim to optimize the predicted human pose sequences from pose estimators while ensuring minimal computational overhead and latency. Prior investigations have primarily concentrated on striking a balance between the two objectives i.e. smoothness and precision while optimizing the predicted pose sequences. However it has come to our attention that the tension between these two objectives can provide additional quality cues about the predicted pose sequences. These cues in turn are able to aid the network in optimizing lower-quality poses. To leverage this quality information we propose a motion refinement network termed SynSP to achieve a Synergy of Smoothness and Precision in the sequence refinement tasks. Moreover SynSP can also address multi-view poses of one person simultaneously fixing inaccuracies in predicted poses through heightened attention to similar poses from other views thereby amplifying the resultant quality cues and overall performance. Compared with previous methods SynSP benefits from both pose quality and multi-view information with a much shorter input sequence length achieving state-of-the-art results among four challenging datasets involving 2D 3D and SMPL pose representations in both single-view and multi-view scenes. Github code: https://github.com/InvertedForest/SynSP.
We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation which aims to adapt a source-trained model to the target domain without accessing the source data. Many works have been proposed to address this challenging problem among which uncertainty based self-training is a predominant approach. However without comprehensive denoising mechanisms they still largely fall into biased estimates when dealing with different domains and confirmation bias. In this paper we observe that pseudo-label noise is mainly contained in unstable samples in which the predictions of most pixels undergo significant variations during self-training. Inspired by this we propose a novel mechanism to denoise unstable samples with stable ones. Specifically we introduce the Stable Neighbor Denoising (SND) approach which effectively discovers highly correlated stable and unstable samples by nearest neighbor retrieval and guides the reliable optimization of unstable samples by bi-level learning. Moreover we compensate for the stable set by object-level object paste which can further eliminate the bias caused by less learned classes. Our SND enjoys two advantages. First SND does not require a specific segmentor structure endowing its universality. Second SND simultaneously addresses the issues of class domain and confirmation biases during adaptation ensuring its effectiveness. Extensive experiments show that SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings. In addition SND can be easily integrated with other approaches obtaining further improvements. The source code will be publicly available.
Steganographic Passport: An Owner and User Verifiable Credential for Deep Model IP Protection Without Retraining
PDF ↗Ensuring the legal usage of deep models is crucial to promoting trustable accountable and responsible artificial intelligence innovation. Current passport-based methods that obfuscate model functionality for license-to-use and ownership verifications suffer from capacity and quality constraints as they require retraining the owner model for new users. They are also vulnerable to advanced Expanded Residual Block ambiguity attacks. We propose Steganographic Passport which uses an invertible steganographic network to decouple license-to-use from ownership verification by hiding the user's identity images into the owner-side passport and recovering them from their respective user-side passports. An irreversible and collision-resistant hash function is used to avoid exposing the owner-side passport from the derived user-side passports and increase the uniqueness of the model signature. To safeguard both the passport and model's weights against advanced ambiguity attacks an activation-level obfuscation is proposed for the verification branch of the owner's model. By jointly training the verification and deployment branches their weights become tightly coupled. The proposed method supports agile licensing of deep models by providing a strong ownership proof and license accountability without requiring a separate model retraining for the admission of every new user. Experiment results show that our Steganographic Passport outperforms other passport-based deep model protection methods in robustness against various known attacks.