CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
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.
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.
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).
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 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 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/.
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.
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%.
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.
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.
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.
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 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
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.
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.
Human perception of the world is shaped by a multitude of viewpoints and modalities. While many existing datasets focus on scene understanding from a certain perspective (e.g. egocentric or third-person views) our dataset offers a panoptic perspective (i.e. multiple viewpoints with multiple data modalities). Specifically we encapsulate third-person panoramic and front views as well as egocentric monocular/binocular views with rich modalities including video multi-channel audio directional binaural delay location data and textual scene descriptions within each scene captured presenting comprehensive observation of the world. To the best of our knowledge this is the first database that covers multiple viewpoints with multiple data modalities to mimic how daily information is accessed in the real world. Through our benchmark analysis we presented 5 different scene understanding tasks on the proposed 360+x dataset to evaluate the impact and benefit of each data modality and perspective in panoptic scene understanding. We hope this unique dataset could broaden the scope of comprehensive scene understanding and encourage the community to approach these problems from more diverse perspectives.
Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. Existing methods learn representations based on trajectory labels for individuals which are annotation-expensive. To provide a more realistic reflection of the underlying practical challenge we introduce a weakly supervised VIC task wherein trajectory labels are not provided. Instead two types of labels are provided to indicate traffic entering the field of view (inflow) and leaving the field view (outflow). We also propose the first solution as a baseline that formulates the task as a weakly supervised contrastive learning problem under group-level matching. In doing so we devise an end-to-end trainable soft contrastive loss to drive the network to distinguish inflow outflow and the remaining. To facilitate future study in this direction we generate annotations from the existing VIC datasets SenseCrowd and CroHD and also build a new dataset UAVVIC. Extensive results show that our baseline weakly supervised method outperforms supervised methods and thus little information is lost in the transition to the more practically relevant weakly supervised task. The code and trained model can be found at CGNet.
Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. However existing methods often compromise the model performance or require additional training which is undesirable for operators and users. To address this issue we propose Gaussian Shading a diffusion model watermarking technique that is both performance-lossless and training-free while serving the dual purpose of copyright protection and tracing of offending content. Our watermark embedding is free of model parameter modifications and thus is plug-and-play. We map the watermark to latent representations following a standard Gaussian distribution which is indistinguishable from latent representations obtained from the non-watermarked diffusion model. Therefore we can achieve watermark embedding with lossless performance for which we also provide theoretical proof. Furthermore since the watermark is intricately linked with image semantics it exhibits resilience to lossy processing and erasure attempts. The watermark can be extracted by Denoising Diffusion Implicit Models (DDIM) inversion and inverse sampling. We evaluate Gaussian Shading on multiple versions of Stable Diffusion and the results demonstrate that Gaussian Shading not only is performance-lossless but also outperforms existing methods in terms of robustness.
Event cameras capture the world at high time resolution and with minimal bandwidth requirements. However event streams which only encode changes in brightness do not contain sufficient scene information to support a wide variety of downstream tasks. In this work we design generalized event cameras that inherently preserve scene intensity in a bandwidth-efficient manner. We generalize event cameras in terms of when an event is generated and what information is transmitted. To implement our designs we turn to single-photon sensors that provide digital access to individual photon detections; this modality gives us the flexibility to realize a rich space of generalized event cameras. Our single-photon event cameras are capable of high-speed high-fidelity imaging at low readout rates. Consequently these event cameras can support plug-and-play downstream inference without capturing new event datasets or designing specialized event-vision models. As a practical implication our designs which involve lightweight and near-sensor-compatible computations provide a way to use single-photon sensors without exorbitant bandwidth costs.
Real-world objects and environments are predominantly composed of edge features including straight lines and curves. Such edges are crucial elements for various applications such as CAD modeling surface meshing lane mapping etc. However existing traditional methods only prioritize lines over curves for simplicity in geometric modeling. To this end we introduce EMAP a new method for learning 3D edge representations with a focus on both lines and curves. Our method implicitly encodes 3D edge distance and direction in Unsigned Distance Functions (UDF) from multi-view edge maps. On top of this neural representation we propose an edge extraction algorithm that robustly abstracts parametric 3D edges from the inferred edge points and their directions. Comprehensive evaluations demonstrate that our method achieves better 3D edge reconstruction on multiple challenging datasets. We further show that our learned UDF field enhances neural surface reconstruction by capturing more details.
Document image restoration is a crucial aspect of Document AI systems as the quality of document images significantly influences the overall performance. Prevailing methods address distinct restoration tasks independently leading to intricate systems and the incapability to harness the potential synergies of multi-task learning. To overcome this challenge we propose DocRes a generalist model that unifies five document image restoration tasks including dewarping deshadowing appearance enhancement deblurring and binarization. To instruct DocRes to perform various restoration tasks we propose a novel visual prompt approach called Dynamic Task-Specific Prompt (DTSPrompt). The DTSPrompt for different tasks comprises distinct prior features which are additional characteristics extracted from the input image. Beyond its role as a cue for task-specific execution DTSPrompt can also serve as supplementary information to enhance the model's performance. Moreover DTSPrompt is more flexible than prior visual prompt approaches as it can be seamlessly applied and adapted to inputs with high and variable resolutions. Experimental results demonstrate that DocRes achieves competitive or superior performance compared to existing state-of-the-art task-specific models. This underscores the potential of DocRes across a broader spectrum of document image restoration tasks. The source code is publicly available at https://github.com/ZZZHANG-jx/DocRes.