--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:784827 - loss:ContrastiveLoss base_model: sentence-transformers/all-mpnet-base-v2 widget: - source_sentence: >- Background: The study addresses the need for effective tools that allow both novice and expert users to analyze the diversity of news coverage about events. It highlights the importance of tailoring the interface to accommodate non-expert users while also considering the insights of journalism-savvy users, indicating a gap in existing systems that cater to varying levels of expertise in news analysis. Contribution: Combine 'a coordinated visualization interface tailored for visualization non-expert users' and sentences: - a method considering lexical relationships - cross-modality self-supervised learning via masked visual language modeling - cognitive models of chaining - source_sentence: >- Background: Existing methods for anomaly detection on dynamic graphs struggle with capturing complex time information in graph structures and generating effective negative samples for unsupervised learning. These challenges highlight the need for improved methodologies that can address the limitations of current approaches in this field. Contribution: Combine 'a message-passing framework' and sentences: - the grouping task - a forecaster - the optimisation algorithm producing the learnable model - source_sentence: >- Background: The accuracy of pixel flows is crucial for achieving high-quality video enhancement, yet most prior works focus on estimating dense flows that are generally less robust and computationally expensive. This highlights a gap in existing methodologies that fail to prioritize accuracy over density, necessitating a more efficient approach to flow estimation for video enhancement tasks. Contribution: Combine 'sparse point cloud data' and sentences: - a deep CNN - a reinforcement learning view of the dialog generation task - graphical models - source_sentence: >- Background: The optimal robot assembly planning problem is challenging due to the necessity of finding the optimal solution amongst an exponentially vast number of possible plans while satisfying a selection of constraints. Traditional heuristic methods are limited as they are specific to a given objective structure or set of problem parameters, indicating a need for more versatile and effective approaches. Contribution: 'pos[e] assembly sequencing' inspired by sentences: - 3D geometric neural field representation - prompt learning - gestures - source_sentence: >- Background: Patients find it difficult to use dexterous prosthetic hands without a suitable control system, highlighting a need for improved grasp performance and ease of operation. Existing methods may not adequately address the challenges faced by users, particularly those with inferior myoelectric signals, in effectively controlling prosthetic devices. Contribution: Combine 'myoelectric signal' and sentences: - >- a unified framework for collaborative decoding between large and small language models (Large Language Models and small language models) - image understanding - joint biomedical entity linking and event extraction pipeline_tag: sentence-similarity library_name: sentence-transformers license: cc datasets: - noystl/Recombination-Pred language: - en --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ "Background: Patients find it difficult to use dexterous prosthetic hands without a suitable control system, highlighting a need for improved grasp performance and ease of operation. Existing methods may not adequately address the challenges faced by users, particularly those with inferior myoelectric signals, in effectively controlling prosthetic devices.\nContribution: Combine 'myoelectric signal' and ", 'a unified framework for collaborative decoding between large and small language models (Large Language Models and small language models)', 'joint biomedical entity linking and event extraction', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 784,827 training samples * Columns: query, answer, and label * Approximate statistics based on the first 1000 samples: | | query | answer | label | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | answer | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:---------------| | Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.
Contribution: Combine 'a Hidden Markov Model' and | a multilayer perceptron | 1 | | Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.
Contribution: Combine 'a Hidden Markov Model' and | synthetic occlusion augmentation during training | 0 | | Background: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.
Contribution: Combine 'a Hidden Markov Model' and | robustness of deep learning methods | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `learning_rate`: 1.9218937402834593e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.08278167292320517 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 1.9218937402834593e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.08278167292320517 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | |:------:|:-----:|:-------------:| | 0.0082 | 100 | 0.0104 | | 0.0163 | 200 | 0.0068 | | 0.0245 | 300 | 0.005 | | 0.0326 | 400 | 0.0041 | | 0.0408 | 500 | 0.0054 | | 0.0489 | 600 | 0.004 | | 0.0571 | 700 | 0.0037 | | 0.0652 | 800 | 0.0037 | | 0.0734 | 900 | 0.0049 | | 0.0815 | 1000 | 0.0038 | | 0.0897 | 1100 | 0.004 | | 0.0979 | 1200 | 0.0037 | | 0.1060 | 1300 | 0.004 | | 0.1142 | 1400 | 0.0049 | | 0.1223 | 1500 | 0.0038 | | 0.1305 | 1600 | 0.0036 | | 0.1386 | 1700 | 0.0037 | | 0.1468 | 1800 | 0.0045 | | 0.1549 | 1900 | 0.0038 | | 0.1631 | 2000 | 0.0034 | | 0.1712 | 2100 | 0.0034 | | 0.1794 | 2200 | 0.0035 | | 0.1876 | 2300 | 0.0045 | | 0.1957 | 2400 | 0.0036 | | 0.2039 | 2500 | 0.0036 | | 0.2120 | 2600 | 0.0033 | | 0.2202 | 2700 | 0.004 | | 0.2283 | 2800 | 0.0036 | | 0.2365 | 2900 | 0.0033 | | 0.2446 | 3000 | 0.0033 | | 0.2528 | 3100 | 0.0037 | | 0.2609 | 3200 | 0.0038 | | 0.2691 | 3300 | 0.0033 | | 0.2773 | 3400 | 0.0034 | | 0.2854 | 3500 | 0.0033 | | 0.2936 | 3600 | 0.0041 | | 0.3017 | 3700 | 0.0033 | | 0.3099 | 3800 | 0.0033 | | 0.3180 | 3900 | 0.0032 | | 0.3262 | 4000 | 0.004 | | 0.3343 | 4100 | 0.0035 | | 0.3425 | 4200 | 0.0031 | | 0.3506 | 4300 | 0.0033 | | 0.3588 | 4400 | 0.0033 | | 0.3670 | 4500 | 0.0039 | | 0.3751 | 4600 | 0.0032 | | 0.3833 | 4700 | 0.0034 | | 0.3914 | 4800 | 0.0031 | | 0.3996 | 4900 | 0.004 | | 0.4077 | 5000 | 0.0032 | | 0.4159 | 5100 | 0.0031 | | 0.4240 | 5200 | 0.0031 | | 0.4322 | 5300 | 0.0032 | | 0.4403 | 5400 | 0.0039 | | 0.4485 | 5500 | 0.0031 | | 0.4567 | 5600 | 0.003 | | 0.4648 | 5700 | 0.0032 | | 0.4730 | 5800 | 0.0038 | | 0.4811 | 5900 | 0.0033 | | 0.4893 | 6000 | 0.0031 | | 0.4974 | 6100 | 0.0032 | | 0.5056 | 6200 | 0.0033 | | 0.5137 | 6300 | 0.0033 | | 0.5219 | 6400 | 0.0032 | | 0.5300 | 6500 | 0.0031 | | 0.5382 | 6600 | 0.0032 | | 0.5464 | 6700 | 0.0038 | | 0.5545 | 6800 | 0.003 | | 0.5627 | 6900 | 0.003 | | 0.5708 | 7000 | 0.0029 | | 0.5790 | 7100 | 0.0038 | | 0.5871 | 7200 | 0.0032 | | 0.5953 | 7300 | 0.0031 | | 0.6034 | 7400 | 0.003 | | 0.6116 | 7500 | 0.003 | | 0.6198 | 7600 | 0.0039 | | 0.6279 | 7700 | 0.0031 | | 0.6361 | 7800 | 0.0031 | | 0.6442 | 7900 | 0.0031 | | 0.6524 | 8000 | 0.0039 | | 0.6605 | 8100 | 0.003 | | 0.6687 | 8200 | 0.003 | | 0.6768 | 8300 | 0.003 | | 0.6850 | 8400 | 0.0028 | | 0.6931 | 8500 | 0.0035 | | 0.7013 | 8600 | 0.0031 | | 0.7095 | 8700 | 0.003 | | 0.7176 | 8800 | 0.0026 | | 0.7258 | 8900 | 0.0034 | | 0.7339 | 9000 | 0.0033 | | 0.7421 | 9100 | 0.003 | | 0.7502 | 9200 | 0.0027 | | 0.7584 | 9300 | 0.0029 | | 0.7665 | 9400 | 0.0034 | | 0.7747 | 9500 | 0.0029 | | 0.7828 | 9600 | 0.0028 | | 0.7910 | 9700 | 0.0027 | | 0.7992 | 9800 | 0.0033 | | 0.8073 | 9900 | 0.0031 | | 0.8155 | 10000 | 0.0029 | | 0.8236 | 10100 | 0.0028 | | 0.8318 | 10200 | 0.0031 | | 0.8399 | 10300 | 0.0031 | | 0.8481 | 10400 | 0.003 | | 0.8562 | 10500 | 0.0029 | | 0.8644 | 10600 | 0.0028 | | 0.8725 | 10700 | 0.0033 | | 0.8807 | 10800 | 0.003 | | 0.8889 | 10900 | 0.0029 | | 0.8970 | 11000 | 0.0027 | | 0.9052 | 11100 | 0.0033 | | 0.9133 | 11200 | 0.0029 | | 0.9215 | 11300 | 0.0029 | | 0.9296 | 11400 | 0.0029 | | 0.9378 | 11500 | 0.003 | | 0.9459 | 11600 | 0.0034 | | 0.9541 | 11700 | 0.0031 | | 0.9622 | 11800 | 0.0027 | | 0.9704 | 11900 | 0.0029 | | 0.9786 | 12000 | 0.0034 | | 0.9867 | 12100 | 0.0032 | | 0.9949 | 12200 | 0.003 | | 1.0030 | 12300 | 0.0032 | | 1.0112 | 12400 | 0.0028 | | 1.0193 | 12500 | 0.003 | | 1.0275 | 12600 | 0.0027 | | 1.0356 | 12700 | 0.0034 | | 1.0438 | 12800 | 0.0029 | | 1.0519 | 12900 | 0.0025 | | 1.0601 | 13000 | 0.0028 | | 1.0683 | 13100 | 0.0026 | | 1.0764 | 13200 | 0.0035 | | 1.0846 | 13300 | 0.0026 | | 1.0927 | 13400 | 0.0028 | | 1.1009 | 13500 | 0.0026 | | 1.1090 | 13600 | 0.0034 | | 1.1172 | 13700 | 0.0028 | | 1.1253 | 13800 | 0.0027 | | 1.1335 | 13900 | 0.0026 | | 1.1416 | 14000 | 0.0031 | | 1.1498 | 14100 | 0.0025 | | 1.1580 | 14200 | 0.0025 | | 1.1661 | 14300 | 0.0025 | | 1.1743 | 14400 | 0.0024 | | 1.1824 | 14500 | 0.0031 | | 1.1906 | 14600 | 0.0025 | | 1.1987 | 14700 | 0.0024 | | 1.2069 | 14800 | 0.0025 | | 1.2150 | 14900 | 0.0029 | | 1.2232 | 15000 | 0.0025 | | 1.2313 | 15100 | 0.0025 | | 1.2395 | 15200 | 0.0023 | | 1.2477 | 15300 | 0.0024 | | 1.2558 | 15400 | 0.0029 | | 1.2640 | 15500 | 0.0023 | | 1.2721 | 15600 | 0.0023 | | 1.2803 | 15700 | 0.0023 | | 1.2884 | 15800 | 0.0032 | | 1.2966 | 15900 | 0.0023 | | 1.3047 | 16000 | 0.0023 | | 1.3129 | 16100 | 0.0024 | | 1.3210 | 16200 | 0.0025 | | 1.3292 | 16300 | 0.0028 | | 1.3374 | 16400 | 0.0023 | | 1.3455 | 16500 | 0.0021 | | 1.3537 | 16600 | 0.0023 | | 1.3618 | 16700 | 0.0029 | | 1.3700 | 16800 | 0.0023 | | 1.3781 | 16900 | 0.0023 | | 1.3863 | 17000 | 0.0025 | | 1.3944 | 17100 | 0.0028 | | 1.4026 | 17200 | 0.0023 | | 1.4107 | 17300 | 0.0023 | | 1.4189 | 17400 | 0.0023 | | 1.4271 | 17500 | 0.0023 | | 1.4352 | 17600 | 0.0029 | | 1.4434 | 17700 | 0.0022 | | 1.4515 | 17800 | 0.0022 | | 1.4597 | 17900 | 0.0023 | | 1.4678 | 18000 | 0.0026 | | 1.4760 | 18100 | 0.0024 | | 1.4841 | 18200 | 0.0023 | | 1.4923 | 18300 | 0.0024 | | 1.5004 | 18400 | 0.0024 | | 1.5086 | 18500 | 0.0026 | | 1.5168 | 18600 | 0.0022 | | 1.5249 | 18700 | 0.0023 | | 1.5331 | 18800 | 0.0023 | | 1.5412 | 18900 | 0.003 | | 1.5494 | 19000 | 0.002 | | 1.5575 | 19100 | 0.0022 | | 1.5657 | 19200 | 0.0023 | | 1.5738 | 19300 | 0.0023 | | 1.5820 | 19400 | 0.0028 | | 1.5901 | 19500 | 0.0022 | | 1.5983 | 19600 | 0.0023 | | 1.6065 | 19700 | 0.0022 | | 1.6146 | 19800 | 0.0028 | | 1.6228 | 19900 | 0.0022 | | 1.6309 | 20000 | 0.0023 | | 1.6391 | 20100 | 0.0025 | | 1.6472 | 20200 | 0.0028 | | 1.6554 | 20300 | 0.0023 | | 1.6635 | 20400 | 0.0021 | | 1.6717 | 20500 | 0.0022 | | 1.6798 | 20600 | 0.0022 | | 1.6880 | 20700 | 0.0025 | | 1.6962 | 20800 | 0.0024 | | 1.7043 | 20900 | 0.0023 | | 1.7125 | 21000 | 0.0021 | | 1.7206 | 21100 | 0.0024 | | 1.7288 | 21200 | 0.0024 | | 1.7369 | 21300 | 0.0023 | | 1.7451 | 21400 | 0.0022 | | 1.7532 | 21500 | 0.0021 | | 1.7614 | 21600 | 0.0025 | | 1.7696 | 21700 | 0.0023 | | 1.7777 | 21800 | 0.002 | | 1.7859 | 21900 | 0.0022 | | 1.7940 | 22000 | 0.0025 | | 1.8022 | 22100 | 0.0022 | | 1.8103 | 22200 | 0.0023 | | 1.8185 | 22300 | 0.0022 | | 1.8266 | 22400 | 0.0021 | | 1.8348 | 22500 | 0.0025 | | 1.8429 | 22600 | 0.0025 | | 1.8511 | 22700 | 0.0022 | | 1.8593 | 22800 | 0.0023 | | 1.8674 | 22900 | 0.0026 | | 1.8756 | 23000 | 0.0022 | | 1.8837 | 23100 | 0.0022 | | 1.8919 | 23200 | 0.0022 | | 1.9000 | 23300 | 0.0024 | | 1.9082 | 23400 | 0.0022 | | 1.9163 | 23500 | 0.0022 | | 1.9245 | 23600 | 0.0023 | | 1.9326 | 23700 | 0.0023 | | 1.9408 | 23800 | 0.0027 | | 1.9490 | 23900 | 0.0023 | | 1.9571 | 24000 | 0.0023 | | 1.9653 | 24100 | 0.0022 | | 1.9734 | 24200 | 0.0027 | | 1.9816 | 24300 | 0.0025 | | 1.9897 | 24400 | 0.0023 | | 1.9979 | 24500 | 0.0025 |
### Framework Versions - Python: 3.11.2 - Sentence Transformers: 3.3.1 - Transformers: 4.49.0 - PyTorch: 2.5.1+cu124 - Accelerate: 1.0.1 - Datasets: 3.1.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX ```bibtex @misc{sternlicht2025chimeraknowledgebaseidea, title={CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature}, author={Noy Sternlicht and Tom Hope}, year={2025}, eprint={2505.20779}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.20779}, } ``` #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ``` **Quick Links** - 🌐 [Project](https://noy-sternlicht.github.io/CHIMERA-Web) - 📃 [Paper](https://arxiv.org/abs/2505.20779) - 🛠️ [Code](https://github.com/noy-sternlicht/CHIMERA-KB)