CVPR 2024 Papers

Lifelong Person Re-identification (L-ReID) aims to learn from sequentially collected data to match a person across different scenes. Once an L-ReID model is updated using new data all historical images in the gallery are required to be re-calculated to obtain new features for testing known as "re-indexing". However it is infeasible when raw images in the gallery are unavailable due to data privacy concerns resulting in incompatible retrieval between the query and the gallery features calculated by different models which causes significant performance degradation. In this paper we focus on a new task called Re-indexing Free Lifelong Person Re-identification (RFL-ReID) which requires achieving effective L-ReID without re-indexing raw images in the gallery. To this end we propose a Continual Compatible Representation (C2R) method which facilitates the query feature calculated by the continuously updated model to effectively retrieve the gallery feature calculated by the old model in a compatible manner. Specifically we design a Continual Compatible Transfer (CCT) network to continuously transfer and consolidate the old gallery feature into the new feature space. Besides a Balanced Compatible Distillation module is introduced to achieve compatibility by aligning the transferred feature space with the new feature space. Finally a Balanced Anti-forgetting Distillation module is proposed to eliminate the accumulated forgetting of old knowledge during the continual compatible transfer. Extensive experiments on several benchmark L-ReID datasets demonstrate the effectiveness of our method against state-of-the-art methods for both RFL-ReID and L-ReID tasks. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/C2R_CVPR2024.

Most domain adaptation (DA) methods are based on either a convolutional neural networks (CNNs) or a vision transformers (ViTs). They align the distribution differences between domains as encoders without considering their unique characteristics. For instance ViT excels in accuracy due to its superior ability to capture global representations while CNN has an advantage in capturing local representations. This fact has led us to design a hybrid method to fully take advantage of both ViT and CNN called Explicitly Class-specific Boundaries (ECB). ECB learns CNN on ViT to combine their distinct strengths. In particular we leverage ViT's properties to explicitly find class-specific decision boundaries by maximizing the discrepancy between the outputs of the two classifiers to detect target samples far from the source support. In contrast the CNN encoder clusters target features based on the previously defined class-specific boundaries by minimizing the discrepancy between the probabilities of the two classifiers. Finally ViT and CNN mutually exchange knowledge to improve the quality of pseudo labels and reduce the knowledge discrepancies of these models. Compared to conventional DA methods our ECB achieves superior performance which verifies its effectiveness in this hybrid model. The project website can be found https://dotrannhattuong.github.io/ECB/website/.

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection significantly generalizing the powerful capabilities of the detector to identify more unknown object categories. However these methods face significant challenges in background interpretation and model overfitting and thus often result in the loss of crucial background knowledge giving rise to sub-optimal inference performance of the detector. To mitigate these issues we present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge thus enhancing the detection performance w.r.t. base and novel categories. Specifically we devise three modules: Background Category-specific Prompt Background Object Discovery and Inference Probability Rectification to empower the detector to discover represent and leverage implicit object knowledge explored from background proposals. Evaluation on two benchmark datasets OV-COCO and OV-LVIS demonstrates the superiority of our proposed method over existing state-of-the-art approaches in handling the OVD tasks.

Clustering multiple motions from observed point trajectories is a fundamental task in understanding dynamic scenes. Most motion models require multiple tracks to estimate their parameters hence identifying clusters when multiple motions are observed is a very challenging task. This is even aggravated for high-dimensional motion models. The starting point of our work is that this high-dimensionality of motion model can actually be leveraged to our advantage as sufficiently long trajectories identify the underlying motion uniquely in practice. Consequently we propose to learn a mapping from trajectories to embedding vectors that represent the generating motion. The obtained trajectory embeddings are useful for clustering multiple observed motions but are also trained to contain sufficient information to recover the parameters of the underlying motion by utilizing a geometric loss. We therefore are able to use only weak supervision from given motion segmentation to train this mapping. The entire algorithm consisting of trajectory embedding clustering and motion parameter estimation is highly efficient. We conduct experiments on the Hopkins155 Hopkins12 and KT3DMoSeg datasets and show state-of-the-art performance of our proposed method for trajectory-based motion segmentation on full sequences and its competitiveness on the occluded sequences. Project page: https://ylochman.github.io/trajectory-embedding.

Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless their spherical nature gives rise to various and uncertain user viewing behaviors which poses significant challenges for panoramic video quality assessment (PVQA). In this work we propose an end-to-end optimized blind PVQA method with explicit modeling of user viewing patterns through visual scanpaths. Our method consists of two modules: a scanpath generator and a quality assessor. The scanpath generator is initially trained to predict future scanpaths by minimizing their expected code length and then jointly optimized with the quality assessor for quality prediction. Our blind PVQA method enables direct quality assessment of panoramic images by treating them as videos composed of identical frames. Experiments on three public panoramic image and video quality datasets encompassing both synthetic and authentic distortions validate the superiority of our blind PVQA model over existing methods.

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.

Autoregressive Initial Bits (ArIB) a framework that combines subimage autoregression and latent variable models has shown its advantages in lossless image compression. However in current methods the image splitting makes the information of latent variables being uniformly distributed in each subimage and causes inadequate use of latent variables in addition to posterior collapse. To tackle these issues we introduce Bit Plane Slicing (BPS) splitting images in the bit plane dimension with the considerations on different importance for latent variables. Thus BPS provides a more effective representation by arranging subimages with decreasing importance for latent variables. To solve the problem of the increased number of dimensions caused by BPS we further propose a dimension-tailored autoregressive model that tailors autoregression methods for each dimension based on their characteristics efficiently capturing the dependencies in plane space and color dimensions. As shown in the extensive experimental results our method demonstrates the superior compression performance with comparable inference speed when compared to the state-of-the-art normalizing-flow-based methods. The code is at https://github.com/ZZ022/ArIB-BPS.

We propose an unsupervised method for parsing large 3D scans of real-world scenes with easily-interpretable shapes. This work aims to provide a practical tool for analyzing 3D scenes in the context of aerial surveying and mapping without the need for user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical 3D shapes. The resulting reconstruction is visually interpretable and can be used to perform unsupervised instance and low-shot semantic segmentation of complex scenes. We demonstrate the usefulness of our model on a novel dataset of seven large aerial LiDAR scans from diverse real-world scenarios. Our approach outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our code and dataset are available at https://romainloiseau.fr/learnable-earth-parser/.

Recent works utilize CLIP to perform the challenging unsupervised semantic segmentation task where only images without annotations are available. However we observe that when adopting CLIP to such a pixel-level understanding task unexpected bias occurs. Previous works don't explicitly model such bias which largely constrains the segmentation performance. In this paper we propose to explicitly model and rectify the bias existing in CLIP to facilitate the unsupervised semantic segmentation. Specifically we design a learnable "Reference" prompt to encode class-preference bias and project the positional embedding of vision transformer to represent space-preference bias. Via a simple element-wise subtraction we rectify the logits of CLIP classifier. Based on the rectified logits we generate a segmentation mask via a Gumbel-Softmax operation. Then a contrastive loss between masked visual feature and the text features of different classes is imposed to facilitate the effective bias modeling. To further improve the segmentation we distill the knowledge from the rectified CLIP to the advanced segmentation architecture via minimizing our designed mask-guided feature-guided and text-guided loss terms. Extensive experiments on standard benchmarks demonstrate that our method performs favorably against previous state-of-the-arts. The implementation is available at https://github.com/dogehhh/ReCLIP.

In recent years anchor-based methods have achieved promising progress in multi-view clustering. The performances of these methods are significantly affected by the quality of the anchors. However the anchors generated by previous works solely rely on single-view information ignoring the correlation among different views. In particular we observe that similar patterns are more likely to exist between similar views so such correlation information can be leveraged to enhance the quality of the anchors which is also omitted. To this end we propose a novel plug-and-play anchor enhancement strategy through view correlation for multi-view clustering. Specifically we construct a view graph based on aligned initial anchor graphs to explore inter-view correlations. By learning from view correlation we enhance the anchors of the current view using the relationships between anchors and samples on neighboring views thereby narrowing the spatial distribution of anchors on similar views. Experimental results on seven datasets demonstrate the superiority of our proposed method over other existing methods. Furthermore extensive comparative experiments validate the effectiveness of the proposed anchor enhancement module when applied to various anchor-based methods.

Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this prior work has shown that an attacker at the server can still reconstruct the private training data using only the client updates. These attacks are known as data reconstruction attacks and fall into two major categories: gradient inversion (GI) and linear layer leakage attacks (LLL). However despite demonstrating the effectiveness of these attacks in breaching privacy prior work has not investigated the usefulness of the reconstructed data for downstream tasks. In this work we explore data reconstruction attacks through the lens of training and improving models with leaked data. We demonstrate the effectiveness of both GI and LLL attacks in maliciously training models using the leaked data more accurately than a benign federated learning strategy. Counter-intuitively this bump in training quality can occur despite limited reconstruction quality or a small total number of leaked images. Finally we show the limitations of these attacks for downstream training individually for GI attacks and for LLL attacks.

Recently leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However previous works that employ LLMs/MLLMs for document understanding have not fully explored and utilized the document layout information which is vital for precise document understanding. In this paper we propose LayoutLLM an LLM/MLLM based method for document understanding. The core of LayoutLLM is a layout instruction tuning strategy which is specially designed to enhance the comprehension and utilization of document layouts. The proposed layout instruction tuning strategy consists of two components: Layout-aware Pre-training and Layout-aware Supervised Fine-tuning. To capture the characteristics of document layout in Layout-aware Pre-training three groups of pre-training tasks corresponding to document-level region-level and segment-level information are introduced. Furthermore a novel module called layout chain-of-thought (LayoutCoT) is devised to enable LayoutLLM to focus on regions relevant to the question and generate accurate answers. LayoutCoT is effective for boosting the performance of document understanding. Meanwhile it brings a certain degree of interpretability which could facilitate manual inspection and correction. Experiments on standard benchmarks show that the proposed LayoutLLM significantly outperforms existing methods that adopt open-source 7B LLMs/MLLMs for document understanding.

Existing scene text detectors generally focus on accurately detecting single-level (i.e. word-level line-level or paragraph-level) text entities without exploring the relationships among different levels of text entities. To comprehensively understand scene texts detecting multi-level texts while exploring their contextual information is critical. To this end we propose a unified framework (dubbed LayoutFormer) for hierarchical text detection which simultaneously conducts multi-level text detection and predicts the geometric layouts for promoting scene text understanding. In LayoutFormer WordDecoder LineDecoder and ParaDecoder are proposed to be responsible for word-level text prediction line-level text prediction and paragraph-level text prediction respectively. Meanwhile WordDecoder and ParaDecoder adaptively learn word-line and line-paragraph relationships respectively. In addition we propose a Prior Location Sampler to be used on multi-scale features to adaptively select a few representative foreground features for updating text queries. It can improve hierarchical detection performance while significantly reducing the computational cost. Comprehensive experiments verify that our method achieves state-of-the-art performance on single-level and hierarchical text detection.

While diffusion models have significantly advanced the quality of image generation their capability to accurately and coherently render text within these images remains a substantial challenge. Conventional diffusion-based methods for scene text generation are typically limited by their reliance on an intermediate layout output. This dependency often results in a constrained diversity of text styles and fonts an inherent limitation stemming from the deterministic nature of the layout generation phase. To address these challenges this paper introduces SceneTextGen a novel diffusion-based model specifically designed to circumvent the need for a predefined layout stage. By doing so SceneTextGen facilitates a more natural and varied representation of text. The novelty of SceneTextGen lies in its integration of three key components: a character-level encoder for capturing detailed typographic properties coupled with a character-level instance segmentation model and a word-level spotting model to address the issues of unwanted text generation and minor character inaccuracies. We validate the performance of our method by demonstrating improved character recognition rates on generated images across different public visual text datasets in comparison to both standard diffusion based methods and text specific methods.

Event cameras with their high temporal resolution dynamic range and low power consumption are particularly good at time-sensitive applications like deblurring and frame interpolation. However their performance is hindered by latency variability especially under low-light conditions and with fast-moving objects. This paper addresses the challenge of latency in event cameras -- the temporal discrepancy between the actual occurrence of changes in the corresponding timestamp assigned by the sensor. Focusing on event-guided deblurring and frame interpolation tasks we propose a latency correction method based on a parameterized latency model. To enable data-driven learning we develop an event-based temporal fidelity to describe the sharpness of latent images reconstructed from events and the corresponding blurry images and reformulate the event-based double integral model differentiable to latency. The proposed method is validated using synthetic and real-world datasets demonstrating the benefits of latency correction for deblurring and interpolation across different lighting conditions.

Low-shot image classification where training images are limited or inaccessible has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability e.g. CLIP. Prompt learning methods built with VL models generate text features from the class names that only have confined class-specific information. Large Language Models (LLMs) with their vast encyclopedic knowledge emerge as the complement. Thus in this paper we discuss the integration of LLMs to enhance pre-trained VL models specifically on low-shot classification. However the domain gap between language and vision blocks the direct application of LLMs. Thus we propose LLaMP Large Language Models as Prompt learners that produces adaptive prompts for the CLIP text encoder establishing it as the connecting bridge. Experiments show that compared with other state-of-the-art prompt learning methods LLaMP yields better performance on both zero-shot generalization and few-shot image classification over a spectrum of 11 datasets. Code will be made available at: https://github.com/zhaohengz/LLaMP.

While replacing Gaussian decoders with a conditional diffusion model enhances the perceptual quality of reconstructions in neural image compression their lack of inductive bias for image data restricts their ability to achieve state-of-the-art perceptual levels. To address this limitation we adopt a non-isotropic diffusion model at the decoder side. This model imposes an inductive bias aimed at distinguishing between frequency contents thereby facilitating the generation of high-quality images. Moreover our framework is equipped with a novel entropy model that accurately models the probability distribution of latent representation by exploiting spatio-channel correlations in latent space while accelerating the entropy decoding step. This channel-wise entropy model leverages both local and global spatial contexts within each channel chunk. The global spatial context is built upon the Transformer which is specifically designed for image compression tasks. The designed Transformer employs a Laplacian-shaped positional encoding the learnable parameters of which are adaptively adjusted for each channel cluster. Our experiments demonstrate that our proposed framework yields better perceptual quality compared to cutting-edge generative-based codecs and the proposed entropy model contributes to notable bitrate savings. The code is available at https://github.com/Atefeh-Khoshtinat/Blur-dissipated-compression.

Composed image retrieval (CIR) task takes a composed query of image and text aiming to search relative images for both conditions. Conventional CIR approaches need a training dataset composed of triplets of query image query text and target image which is very expensive to collect. Several recent works have worked on the zero-shot (ZS) CIR paradigm to tackle the issue without using pre-collected triplets. However the existing ZS-CIR methods show limited backbone scalability and generalizability due to the lack of diversity of the input texts during training. We propose a novel CIR framework only using language for its training. Our LinCIR (Language-only training for CIR) can be trained only with text datasets by a novel self-supervision named self-masking projection (SMP). We project the text latent embedding to the token embedding space and construct a new text by replacing the keyword tokens of the original text. Then we let the new and original texts have the same latent embedding vector. With this simple strategy LinCIR is surprisingly efficient and highly effective; LinCIR with CLIP ViT-G backbone is trained in 48 minutes and shows the best ZS-CIR performances on four different CIR benchmarks CIRCO GeneCIS FashionIQ and CIRR even outperforming supervised method on FashionIQ. Code is available at https://github.com/navervision/lincir

This paper studies the problem of language-guided reflection separation which aims at addressing the ill-posed reflection separation problem by introducing language descriptions to provide layer content. We propose a unified framework to solve this problem which leverages the cross-attention mechanism with contrastive learning strategies to construct the correspondence between language descriptions and image layers. A gated network design and a randomized training strategy are employed to tackle the recognizable layer ambiguity. The effectiveness of the proposed method is validated by the significant performance advantage over existing reflection separation methods on both quantitative and qualitative comparisons.