CVPR 2024 Papers

Visual prompting of large vision language models such as CLIP exhibits intriguing zero-shot capabilities. A manually drawn red circle commonly used for highlighting can guide CLIP's attention to the surrounding region to identify specific objects within an image. Without precise object proposals however it is insufficient for localization. Our novel simple yet effective approach i.e. Differentiable Visual Prompting enables CLIP to zero-shot localize: given an image and a text prompt describing an object we first pick a rendered ellipse from uniformly distributed anchor ellipses on the image grid via visual prompting then use three loss functions to tune the ellipse coefficients to encapsulate the target region gradually. This yields promising experimental results for referring expression comprehension without precisely specified object proposals. In addition we systematically present the limitations of visual prompting inherent in CLIP and discuss potential solutions.

Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions these methods ignore the explicit modeling of the state and object thus limiting the exploitation of pre-trained knowledge and generalization to unseen compositions. With a particular focus on the universality of the solution in this work we propose a novel paradigm for CZSL models that establishes three identification branches (i.e. Multi-Path) to jointly model the state object and composition. The presented Troika is an outstanding implementation that aligns the branch-specific prompt representations with decomposed visual features. To calibrate the bias between semantically similar multi-modal representations we further devise a Cross-Modal Traction module into Troika that shifts the prompt representation towards the current visual content. We conduct extensive experiments on three popular benchmarks where our method significantly outperforms existing methods in both closed-world and open-world settings. The code will be available at https://github.com/bighuang624/Troika.

Recent success of pre-trained foundation vision-language models makes Open-Vocabulary Segmentation (OVS) possible. Despite the promising performance this approach introduces heavy computational overheads for two challenges: 1) large model sizes of the backbone; 2) expensive costs during the fine-tuning. These challenges hinder this OVS strategy from being widely applicable and affordable in real-world scenarios. Although traditional methods such as model compression and efficient fine-tuning can address these challenges they often rely on heuristics. This means that their solutions cannot be easily transferred and necessitate re-training on different models which comes at a cost. In the context of efficient OVS we target achieving performance that is comparable to or even better than prior OVS works based on large vision-language foundation models by utilizing smaller models that incur lower training costs. The core strategy is to make our efficiency principled and thus seamlessly transferable from one OVS framework to others without further customization. Comprehensive experiments on diverse OVS benchmarks demonstrate our superior trade-off between segmentation accuracy and computation costs over previous works. Our code is available on https://github.com/Xujxyang/OpenTrans

Image denoising is a fundamental task in computer vision. While prevailing deep learning-based supervised and self-supervised methods have excelled in eliminating in-distribution noise their susceptibility to out-of-distribution (OOD) noise remains a significant challenge. The recent emergence of contrastive language-image pre-training (CLIP) model has showcased exceptional capabilities in open-world image recognition and segmentation. Yet the potential for leveraging CLIP to enhance the robustness of low-level tasks remains largely unexplored. This paper uncovers that certain dense features extracted from the frozen ResNet image encoder of CLIP exhibit distortion-invariant and content-related properties which are highly desirable for generalizable denoising. Leveraging these properties we devise an asymmetrical encoder-decoder denoising network which incorporates dense features including the noisy image and its multi-scale features from the frozen ResNet encoder of CLIP into a learnable image decoder to achieve generalizable denoising. The progressive feature augmentation strategy is further proposed to mitigate feature overfitting and improve the robustness of the learnable decoder. Extensive experiments and comparisons conducted across diverse OOD noises including synthetic noise real-world sRGB noise and low-dose CT image noise demonstrate the superior generalization ability of our method.

Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks (DNNs) especially Transformers for super-resolution have seen significant advancements in recent years challenges still remain particularly in limited receptive field caused by window-based self-attention. To address these issues we introduce a group of auxiliary Adaptive Token Dictionary to SR Transformer and establish an ATD-SR method. The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step. The refinement strategy could not only provide global information to all input tokens but also group image tokens into categories. Based on category partitions we further propose a category-based self-attention mechanism designed to leverage distant but similar tokens for enhancing input features. The experimental results show that our method achieves the best performance on various single image super-resolution benchmarks.

Due to the depth degradation effect in residual connections many efficient Vision Transformers models that rely on stacking layers for information exchange often fail to form sufficient information mixing leading to unnatural visual perception. To address this issue in this paper we propose Aggregated Attention a biomimetic design-based token mixer that simulates biological foveal vision and continuous eye movement while enabling each token on the feature map to have a global perception. Furthermore we incorporate learnable tokens that interact with conventional queries and keys which further diversifies the generation of affinity matrices beyond merely relying on the similarity between queries and keys. Our approach does not rely on stacking for information exchange thus effectively avoiding depth degradation and achieving natural visual perception. Additionally we propose Convolutional GLU a channel mixer that bridges the gap between GLU and SE mechanism which empowers each token to have channel attention based on its nearest neighbor image features enhancing local modeling capability and model robustness. We combine aggregated attention and convolutional GLU to create a new visual backbone called TransNeXt. Extensive experiments demonstrate that our TransNeXt achieves state-of-the-art performance across multiple model sizes. At a resolution of 224^2 TransNeXt-Tiny attains an ImageNet accuracy of 84.0% surpassing ConvNeXt-B with 69% fewer parameters. Our TransNeXt-Base achieves an ImageNet accuracy of 86.2% and an ImageNet-A accuracy of 61.6% at a resolution of 384^2 a COCO object detection mAP of 57.1 and an ADE20K semantic segmentation mIoU of 54.7.

We present S4Former a novel approach to training Vision Transformers for Semi-Supervised Semantic Segmentation (S4). At its core S4Former employs a Vision Transformer within a classic teacher-student framework and then leverages three novel technical ingredients: PatchShuffle as a parameter-free perturbation technique Patch-Adaptive Self-Attention (PASA) as a fine-grained feature modulation method and the innovative Negative Class Ranking (NCR) regularization loss. Based on these regularization modules aligned with Transformer-specific characteristics across the image input feature and output dimensions S4Former exploits the Transformer's ability to capture and differentiate consistent global contextual information in unlabeled images. Overall S4Former not only defines a new state of the art in S4 but also maintains a streamlined and scalable architecture. Being readily compatible with existing frameworks S4Former achieves strong improvements (up to 4.9%) on benchmarks like Pascal VOC 2012 COCO and Cityscapes with varying numbers of labeled data. The code is at https://github.com/JoyHuYY1412/S4Former.

Surveillance videos are important for public security. However current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory semantic understanding although they have obtained considerable performance. To address this issue we propose a new research direction of surveillance video-and-language understanding(VALU) and construct the first multimodal surveillance video dataset. We manually annotate the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset UCA (UCF-Crime Annotation) contains 23542 sentences with an average length of 20 words and its annotated videos are as long as 110.7 hours. Furthermore we benchmark SOTA models for four multimodal tasks on this newly created dataset which serve as new baselines for surveillance VALU. Through experiments we find that mainstream models used in previously public datasets perform poorly on surveillance video demonstrating new challenges in surveillance VALU. We also conducted experiments on multimodal anomaly detection. These results demonstrate that our multimodal surveillance learning can improve the performance of anomaly detection. All the experiments highlight the necessity of constructing this dataset to advance surveillance AI.

Image warping a classic task in computer vision aims to use geometric transformations to change the appearance of images. Recent methods learn the resampling kernels for warping through neural networks to estimate missing values in irregular grids which however fail to capture local variations in deformed content and produce images with distortion and less high-frequency details. To address this issue this paper proposes an effective method namely MFR to learn Multi-Frequency Representations from input images for image warping. Specifically we propose a progressive filtering network to learn image representations from different frequency subbands and generate deformable images in a coarse-to-fine manner. Furthermore we employ learnable Gabor wavelet filters to improve the model's capability to learn local spatial-frequency representations. Comprehensive experiments including homography transformation equirectangular to perspective projection and asymmetric image super-resolution demonstrate that the proposed MFR significantly outperforms state-of-the-art image warping methods. Our method also showcases superior generalization to out-of-distribution domains where the generated images are equipped with rich details and less distortion thereby high visual quality. The source code is available at https://github.com/junxiao01/MFR.

We introduce a new task -- language-driven video inpainting which uses natural language instructions to guide the inpainting process. This approach overcomes the limitations of traditional video inpainting methods that depend on manually labeled binary masks a process often tedious and labor-intensive. We present the Remove Objects from Videos by Instructions (ROVI) dataset containing 5650 videos and 9091 inpainting results to support training and evaluation for this task. We also propose a novel diffusion-based language-driven video inpainting framework the first end-to-end baseline for this task integrating Multimodal Large Language Models to understand and execute complex language-based inpainting requests effectively. Our comprehensive results showcase the dataset's versatility and the model's effectiveness in various language-instructed inpainting scenarios. We have made datasets code and models publicly available at https://github.com/jianzongwu/Language-Driven-Video-Inpainting.

We develop a novel vectorized image representation scheme accommodating both shape/geometry and texture in a decoupled way particularly tailored for reconstruction and editing tasks of artistic/design images such as Emojis and Cliparts. In the heart of this representation is a set of sparsely and unevenly located 2D control points. On one hand these points constitute a collection of parametric/vectorized geometric primitives (e.g. curves and closed shapes) describing the shape characteristics of the target image. On the other hand local texture codes in terms of implicit neural network parameters are spatially distributed into each control point yielding local coordinate-to-RGB mappings within the anchored region of each control point. In the meantime a zero-shot learning algorithm is developed to decompose an arbitrary raster image into the above representation for the sake of high-fidelity image vectorization with convenient editing ability. Extensive experiments on a series of image vectorization and editing tasks well demonstrate the high accuracy offered by our proposed method with a significantly higher image compression ratio over prior art.

The spiking cameras offer the benefits of high dynamic range (HDR) high temporal resolution and low data redundancy. However reconstructing HDR videos in high-speed conditions using single-bit spikings presents challenges due to the limited bit depth. Increasing the bit depth of the spikings is advantageous for boosting HDR performance but the readout efficiency will be decreased which is unfavorable for achieving a high frame rate (HFR) video. To address these challenges we propose a readout mechanism to obtain rolling-mixed-bit (RMB) spikings which involves interleaving multi-bit spikings within the single-bit spikings in a rolling manner thereby combining the characteristics of high bit depth and efficient readout. Furthermore we introduce RMB-Net for reconstructing HDR and HFR videos. RMB-Net comprises a cross-bit attention block for fusing mixed-bit spikings and a cross-time attention block for achieving temporal fusion. Extensive experiments conducted on synthetic and real-synthetic data demonstrate the superiority of our method. For instance pure 3-bit spikings result in 3 times of data volume whereas our method achieves comparable performance with less than 2% increase in data volume.

6DoF Pose estimation has been gaining increased importance in vision for over a decade however it does not yet meet the reliability and accuracy standards for mass deployment in industrial robotics. To this effect we present the Industrial Plenoptic Dataset (IPD): the first dataset for the co-evaluation of cameras HDR and algorithms targeted at reliable high-accuracy industrial automation. Specifically we capture 2300 physical scenes of 20 industrial parts covering a 1mx1mx0.5m working volume resulting in over 100000 distinct object views. Each scene is captured with 13 well-calibrated multi-modal cameras including polarization and high-resolution structured light. In terms of lighting we capture each scene at 4 exposures and in 3 challenging lighting conditions ranging from 100 lux to 100000 lux. We also present validate and analyze robot consistency an evaluation method targeted at scalable high accuracy evaluation. We hope that vision systems that succeed on this dataset will have direct industry impact. The dataset and evaluation code are available at https://github.com/intrinsic-ai/ipd.

Vision-language (VL) models have achieved unprecedented success recently in which the connection module is the key to bridge the modality gap. Nevertheless the abundant visual clues are not sufficiently exploited in most existing methods. On the vision side most existing approaches only use the last feature of the vision tower without using the low-level features. On the language side most existing methods only introduce shallow vision-language interactions. In this paper we present a vision-inspired vision-language connection module dubbed as VIVL which efficiently exploits the vision cue for VL models. To take advantage of the lowerlevel information from the vision tower a feature pyramid extractor (FPE) is introduced to combine features from different intermediate layers which enriches the visual cue with negligible parameters and computation overhead. To enhance VL interactions we propose deep vision-conditioned prompts (DVCP) that allows deep interactions of vision and language features efficiently. Our VIVL exceeds the previous state-of-the-art method by 18.1 CIDEr when training from scratch on the COCO caption task which greatly improves the data efficiency. When used as a plug-in module VIVL consistently improves the performance for various backbones and VL frameworks delivering new state-of-the-art results on multiple benchmarks e.g. NoCaps and VQAv2.

We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature energy-efficient low-cost single-photon cameras. These cameras operating as time resolved image sensors illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process account for its non-idealities and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning and offers a step towards 3D vision with single-photon cameras.

While Transformers have rapidly gained popularity in various computer vision applications post-hoc explanations of their internal mechanisms remain largely unexplored. Vision Transformers extract visual information by representing image regions as transformed tokens and integrating them via attention weights. However existing post-hoc explanation methods merely consider these attention weights neglecting crucial information from the transformed tokens which fails to accurately illustrate the rationales behind the models' predictions. To incorporate the influence of token transformation into interpretation we propose TokenTM a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects. Specifically we quantify token transformation effects by measuring changes in token lengths and correlations in their directions pre- and post-transformation. Moreover we develop initialization and aggregation rules to integrate both attention weights and token transformation effects across all layers capturing holistic token contributions throughout the model. Experimental results on segmentation and perturbation tests demonstrate the superiority of our proposed TokenTM compared to state-of-the-art Vision Transformer explanation methods.

This work proposes TimeChat a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contributions: (1) a timestamp-aware frame encoder that binds visual content with the timestamp of each frame and (2) a sliding video Q-Former that produces a video token sequence of varying lengths to accommodate videos of various durations. Additionally we construct an instruction-tuning dataset encompassing 6 tasks and a total of 125K instances to further enhance TimeChat's instruction-following performance. Experiment results across various video understanding tasks such as dense captioning temporal grounding and highlight detection demonstrate TimeChat's strong zero-shot temporal localization and reasoning capabilities. For example it achieves +9.2 F1 score and +2.8 CIDEr on YouCook2 +5.8 HIT@1 on QVHighlights and +27.5 R@1 (IoU=0.5) on Charades-STA compared to state-of-the-art video large language models holding the potential to serve as a versatile video assistant for long-form video comprehension tasks and satisfy realistic user requirements.

Despite many attempts to leverage pre-trained text-to-image models (T2I) like Stable Diffusion (SD) for controllable image editing producing good predictable results remains a challenge. Previous approaches have focused on either fine-tuning pre-trained T2I models on specific datasets to generate certain kinds of images (e.g. with a specific object or person) or on optimizing the weights text prompts and/or learning features for each input image in an attempt to coax the image generator to produce the desired result. However these approaches all have shortcomings and fail to produce good results in a predictable and controllable manner. To address this problem we present TiNO-Edit an SD-based method that focuses on optimizing the noise patterns and diffusion timesteps during editing something previously unexplored in the literature. With this simple change we are able to generate results that both better align with the original images and reflect the desired result. Furthermore we propose a set of new loss functions that operate in the latent domain of SD greatly speeding up the optimization when compared to prior losses which operate in the pixel domain. Our method can be easily applied to variations of SD including Textual Inversion and DreamBooth that encode new concepts and incorporate them into the edited results. We present a host of image-editing capabilities enabled by our approach. Our code is publicly available at https://github.com/SherryXTChen/TiNO-Edit.

Self-supervised image backbones can be used to address complex 2D tasks (e.g. semantic segmentation object discovery) very efficiently and with little or no downstream supervision. Ideally 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results obtained thanks to distillation methods that keep improving. Yet we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work instead of focusing only on the distillation method we study the effect of three pillars for distillation: the 3D backbone the pretrained 2D backbone and the pretraining 2D+3D dataset. In particular thanks to our scalable distillation method named ScaLR we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features and to improve the robustness of the pretrained backbones to domain gaps and perturbations.