GaitSTR: Gait Recognition with Sequential Two-stream Refinement

Gait recognition aims to identify a person based on their walking sequences, serving as a useful biometric modality as it can be observed from long distances without requiring cooperation from the subject. In representing a person's walking sequence, silhouettes and skeletons are the two primary modalities used. Silhouette sequences lack detailed part information when overlapping occurs between different body segments and are affected by carried objects and clothing. Skeletons, comprising joints and bones connecting the joints, provide more accurate part information for different segments; however, they are sensitive to occlusions and low-quality images, causing inconsistencies in frame-wise results within a sequence. In this paper, we explore the use of a two-stream representation of skeletons for gait recognition, alongside silhouettes. By fusing the combined data of silhouettes and skeletons, we refine the two-stream skeletons, joints, and bones through self-correction in graph convolution, along with cross-modal correction with temporal consistency from silhouettes. We demonstrate that with refined skeletons, the performance of the gait recognition model can achieve further improvement on public gait recognition datasets compared with state-of-the-art methods without extra annotations.

Large Language Models are Good Prompt Learners for Low-Shot Image Classification

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.

ShARc: Shape and Appearance Recognition for Person Identification In-the-wild

Identifying individuals in unconstrained video settings is a valuable yet challenging task in biometric analysis due to variations in appearances, environments, degradations, and occlusions. In this paper, we present ShARc, a multimodal approach for video-based person identification in uncontrolled environments that emphasizes 3-D body shape, pose, and appearance. We introduce two encoders: a Pose and Shape Encoder (PSE) and an Aggregated Appearance Encoder (AAE). PSE encodes the body shape via binarized silhouettes, skeleton motions, and 3-D body shape, while AAE provides two levels of temporal appearance feature aggregation: attention-based feature aggregation and averaging aggregation. For attention-based feature aggregation, we employ spatial and temporal attention to focus on key areas for person distinction. For averaging aggregation, we introduce a novel flattening layer after averaging to extract more distinguishable information and reduce overfitting of attention. We utilize centroid feature averaging for gallery registration. We demonstrate significant improvements over existing state-of-the-art methods on public datasets, including CCVID, MEVID, and BRIAR.

CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning

Compositionality, the ability to combine existing concepts and generalize towards novel compositions, is a key functionality for intelligent entities. Here, we study the problem of Compositional Zero-Shot Learning (CZSL), which aims at recognizing novel attribute-object compositions. Recent approaches build their systems on top of large-scale Vision-Language Pre-trained (VLP) models, e.g. CLIP, and observe significant improvements. However, these methods treat CLIP as a black box and focus on pre- and post-CLIP operations. Here, we propose to dive deep into the architecture and insert adapters, a parameter-efficient technique proven to be effective among large language models, to each CLIP encoder layer. We further equip adapters with concept awareness so that concept-specific features of "object", "attribute" and "composition" can be extracted. We name our method CAILA, Concept-Aware Intra-Layer Adapters. Quantitative evaluations performed on three popular CZSL datasets, MIT-States, C-GQA, and UT-Zappos, reveal that CAILA achieves double-digit relative improvements against the current state-of-the-art on all benchmarks.

CAT-NeRF: Constancy-Aware Tx$^2$Former for Dynamic Body Modeling

This paper addresses the problem of human rendering in the video with temporal appearance constancy. Reconstructing dynamic body shapes with volumetric neural rendering methods, such as NeRF, requires finding the correspondence of the points in the canonical and observation space, which demands understanding human body shape and motion. Some methods use rigid transformation, such as SE(3), which cannot precisely model each frame's unique motion and muscle movements. Others generate the transformation for each frame with a trainable network, such as neural blend weight field or translation vector field, which does not consider the appearance constancy of general body shape. In this paper, we propose CAT-NeRF for self-awareness of appearance constancy with Tx$^2$Former, a novel way to combine two Transformer layers, to separate appearance constancy and uniqueness. Appearance constancy models the general shape across the video, and uniqueness models the unique patterns for each frame. We further introduce a novel Covariance Loss to limit the correlation between each pair of appearance uniquenesses to ensure the frame-unique pattern is maximally captured in appearance uniqueness. We assess our method on H36M and ZJU-MoCap and show state-of-the-art performance.

GaitRef: Gait Recognition with Refined Sequential Skeletons

Identifying humans with their walking sequences, known as gait recognition, is a useful biometric understanding task as it can be observed from a long distance and does not require cooperation from the subject. Two common modalities used for representing the walking sequence of a person are silhouettes and joint skeletons. Silhouette sequences, which record the boundary of the walking person in each frame, may suffer from the variant appearances from carried-on objects and clothes of the person. Framewise joint detections are noisy and introduce some jitters that are not consistent with sequential detections. In this paper, we combine the silhouettes and skeletons and refine the framewise joint predictions for gait recognition. With temporal information from the silhouette sequences. We show that the refined skeletons can improve gait recognition performance without extra annotations. We compare our methods on four public datasets, CASIA-B, OUMVLP, Gait3D and GREW, and show state-of-the-art performance.

MoMo: A shared encoder Model for text, image and multi-Modal representations

We propose a self-supervised shared encoder model that achieves strong results on several visual, language and multimodal benchmarks while being data, memory and run-time efficient. We make three key contributions. First, in contrast to most existing works, we use a single transformer with all the encoder layers processing both the text and the image modalities. Second, we propose a stage-wise training strategy where the model is first trained on images, then jointly with unimodal text and image datasets and finally jointly with text and text-image datasets. Third, to preserve information across both the modalities, we propose a training pipeline that learns simultaneously from gradient updates of different modalities at each training update step. The results on downstream text-only, image-only and multimodal tasks show that our model is competitive with several strong models while using fewer parameters and lesser pre-training data. For example, MoMo performs competitively with FLAVA on multimodal (+3.1), image-only (+1.1) and text-only (-0.1) tasks despite having 2/5th the number of parameters and using 1/3rd the image-text training pairs. Finally, we ablate various design choices and further show that increasing model size produces significant performance gains indicating potential for substantial improvements with larger models using our approach.

Gait Recognition Using 3-D Human Body Shape Inference

Gait recognition, which identifies individuals based on their walking patterns, is an important biometric technique since it can be observed from a distance and does not require the subject's cooperation. Recognizing a person's gait is difficult because of the appearance variants in human silhouette sequences produced by varying viewing angles, carrying objects, and clothing. Recent research has produced a number of ways for coping with these variants. In this paper, we present the usage of inferring 3-D body shapes distilled from limited images, which are, in principle, invariant to the specified variants. Inference of 3-D shape is a difficult task, especially when only silhouettes are provided in a dataset. We provide a method for learning 3-D body inference from silhouettes by transferring knowledge from 3-D shape prior from RGB photos. We use our method on multiple existing state-of-the-art gait baselines and obtain consistent improvements for gait identification on two public datasets, CASIA-B and OUMVLP, on several variants and settings, including a new setting of novel views not seen during training.

PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch

Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticker) to the real-world objects. Despite the diversity in attack patterns, adversarial patches tend to be highly textured and different in appearance from natural images. We exploit this property and present PatchZero, a general defense pipeline against white-box adversarial patches without retraining the downstream classifier or detector. Specifically, our defense detects adversaries at the pixel-level and "zeros out" the patch region by repainting with mean pixel values. We further design a two-stage adversarial training scheme to defend against the stronger adaptive attacks. PatchZero achieves SOTA defense performance on the image classification (ImageNet, RESISC45), object detection (PASCAL VOC), and video classification (UCF101) tasks with little degradation in benign performance. In addition, PatchZero transfers to different patch shapes and attack types.

Improving Object Detection and Attribute Recognition by Feature Entanglement Reduction

We explore object detection with two attributes: color and material. The task aims to simultaneously detect objects and infer their color and material. A straight-forward approach is to add attribute heads at the very end of a usual object detection pipeline. However, we observe that the two goals are in conflict: Object detection should be attribute-independent and attributes be largely object-independent. Features computed by a standard detection network entangle the category and attribute features; we disentangle them by the use of a two-stream model where the category and attribute features are computed independently but the classification heads share Regions of Interest (RoIs). Compared with a traditional single-stream model, our model shows significant improvements over VG-20, a subset of Visual Genome, on both supervised and attribute transfer tasks.